Human Volunteer 16S Amplicon Analysis
Barry Hykes Jr. and Scott A. Handley
June 23, 2018
Project Description: Analysis of 16S rRNA amplicon data from a cohort of human volunteers (heatlhy men aged 18-35) randomized into three treatment groups: 1) control (no antibiotics), 2) narrow spectrum (oral vancomycin) or 3) broad-spectrum (oral vancomycin, ciprofloxacin, and metronidazole) antibiotics. Antibiotics were taken for 7-days prior to vaccination with Rotarix (RVV), polysaccharide-pneumococcal (Pneumo 23) and tetanus-toxoid vaccine. The primary endpoint was difference in 28 days-post-vaccination anti-RV IgA. Secondary endpoints were proportion of volunteers with day 7 anti-RV IgA boosting (>=2 fold-increase), absolute and proportion of RV-antigen shedding, anti-RV, pneumococcal and anti-tetanus IgG.
Primary Collaborator: Vanessa Harris (v.harris@aighd.org)
Other relevant files: The following Phyloseq objects are available. Each is distinguished based on the 16S reference database used for taxonomic classification. RDP and Silva were processed through the species assignment workflow:
- ps0.human_volunteer.gg.RDS
- ps0.human_volunteer.silva.RDS
ps0.human_volunteer.rdp.RDS
Mapping file: mapping_human_volunteer.txt
Workflow details: The R commands below represent a full analysis of the following:
- Sample assessment
- ASV properties
- Community composition
- Alpha diversity
- Beta diversity
- Differential abundance testing
# Set default knitr option
knitr::opts_chunk$set(fig.width=8,
fig.height=6,
fig.path="./figures/",
dev='png',
warning=FALSE,
message=FALSE)
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## aperm, apply## [1] '1.21.1'# ggplot2 settings
# Set global theming
theme_set(theme_bw(base_size = 12))
# Set a seed value so that exact results can be reproduced
set.seed(1000)Read in data
# Load Phyloseq Object
# Selected RDP due to it's up-to-date nature and conservative taxonomy. Other files are also valid for anlysis but are not explored here but are available in the ./data/ directory
ps0 <- readRDS("~/Dropbox/Research/human_volunteer/data/ps0.human_volunteer.rdp.RDS")
ps0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1936 taxa and 122 samples ]
## tax_table() Taxonomy Table: [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]# Load mapping file
map <- import_qiime_sample_data("~/Dropbox/Research/human_volunteer/data/mapping_human_volunteer.txt")
ps0 <- merge_phyloseq(ps0, map)
ps0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1936 taxa and 122 samples ]
## sample_data() Sample Data: [ 122 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]# View sample variables & generate basic stats
sample_variables(ps0)## [1] "X.SampleID" "BarcodeSequence"
## [3] "LinkerPrimerSequence" "sequence_ID"
## [5] "miseq_run" "plate"
## [7] "well" "collaborator"
## [9] "tissue" "number_seqs_slout"
## [11] "sample_surviving_dada2" "sample_ID"
## [13] "tube_ID" "patient_ID"
## [15] "randomization" "day"
## [17] "randomization_arm" "antibiotics"
## [19] "Date_assessment" "Age"
## [21] "Gender" "Ethnicity"
## [23] "Asthma" "Eczema"
## [25] "Hayfever" "Allergies"
## [27] "Def_allergies" "Other_illness"
## [29] "Chronic_medication" "Def_medication"
## [31] "Herbs_OTC" "def_Herbs_OTC"
## [33] "Alcohol" "Alcoholunitsweek"
## [35] "Smoking" "Drugs"
## [37] "Drugstype" "Drugsusage"
## [39] "Weight" "Length"
## [41] "BMIcalc" "SystolicBP"
## [43] "DiastolicBP" "Pulse"
## [45] "Respfreq" "Physical_exam_abnorm"
## [47] "def_phys_abnormalities" "Lab_abnorm_screen"
## [49] "def_abnorm_lab_screen" "Serum_screen"
## [51] "PBMC_screen" "Feces_screen"
## [53] "Date_day_9" "New_medication_day_9"
## [55] "def_new_med_day_9" "Course_completed"
## [57] "Antibiotic_side_effects" "Def_abx_side_effects"
## [59] "Lab_abnorm_post_antibiotics" "def_abnorm_post_antibiotics"
## [61] "Date_day_0" "Feces_day_0"
## [63] "New_medication_day0" "Def_new_medication_day_0"
## [65] "Vaccination_side_effects" "Def_vac_side_effects"
## [67] "Lab_abnorm_post_vaccination" "def_abnorm_post_vaccination"
## [69] "Date_day_7" "Feces_day_1"
## [71] "Feces_day_2" "Feces_day_3"
## [73] "Feces_day_4" "Feces_day_5"
## [75] "Feces_day_6" "Feces_day_7"
## [77] "Serum_day_7" "New_medication_day7"
## [79] "def_new_med_day7" "Date_day_14"
## [81] "New_medication_day14" "def_new_med_day14"
## [83] "Serum_day_14" "PBMC_day_14"
## [85] "Date_day_28" "New_medication_day28"
## [87] "def_new_med_day28" "Serum_day_28"
## [89] "PBMC_day_28" "Study_complete"
## [91] "pre_RV_IgA" "d7_RV_IgA"
## [93] "boost_change" "d7_RV_IgA_boost_fc"
## [95] "d7_roto_boost" "d7_rota_boost_updated"
## [97] "d14_RV_IgA" "d28_RV_IgA"
## [99] "RV_Ag_shedding_d0" "RV_Ag_shedding_d1"
## [101] "RV_Ag_shedding_d2" "RV_Ag_shedding_d3"
## [103] "RV_Ag_shedding_d4" "RV_Ag_shedding_d5"
## [105] "RV_Ag_shedding_d6" "Shedding"
## [107] "pneumo_IgG_pre" "pneumo_IgG_7"
## [109] "pneumo_IgG_14" "pneumo_IgG_28"
## [111] "pneumo_IgG_7vPre" "pneumo_IgG_14vPre"
## [113] "pneumo_IgG_28vPre" "tetanus_IgG_pre"
## [115] "tetanus_IgG_d7" "tetanus_IgG_d14"
## [117] "tetanus_IgG_d28" "tetanus_IgG_7vPre"
## [119] "tetanus_IgG_14vPre" "tetanus_IgG_28vPre"sd(sample_sums(ps0))## [1] 23974.15get_taxa_unique(ps0, "Phylum")## [1] "Bacteroidetes" "Firmicutes"
## [3] "Verrucomicrobia" "Proteobacteria"
## [5] "Fusobacteria" "Synergistetes"
## [7] "Actinobacteria" "Spirochaetes"
## [9] NA "Lentisphaerae"
## [11] "Euryarchaeota" "Elusimicrobia"
## [13] "Cyanobacteria/Chloroplast" "Tenericutes"ntaxa(ps0)## [1] 1936Factor reordering and renaming
# Order and update randomization arms factor
sample_data(ps0)$randomization_arm## [1] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [3] No_antibiotics Broad_spectrum_antibiotics
## [5] No_antibiotics No_antibiotics
## [7] No_antibiotics Narrow_spectrum_antibiotics
## [9] No_antibiotics Broad_spectrum_antibiotics
## [11] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [13] Narrow_spectrum_antibiotics No_antibiotics
## [15] Narrow_spectrum_antibiotics No_antibiotics
## [17] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [19] No_antibiotics Broad_spectrum_antibiotics
## [21] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [23] Broad_spectrum_antibiotics No_antibiotics
## [25] Narrow_spectrum_antibiotics No_antibiotics
## [27] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [29] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [31] Narrow_spectrum_antibiotics No_antibiotics
## [33] No_antibiotics Broad_spectrum_antibiotics
## [35] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [37] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [39] No_antibiotics No_antibiotics
## [41] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [43] Broad_spectrum_antibiotics No_antibiotics
## [45] No_antibiotics No_antibiotics
## [47] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [49] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [51] No_antibiotics Broad_spectrum_antibiotics
## [53] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [55] No_antibiotics No_antibiotics
## [57] Narrow_spectrum_antibiotics No_antibiotics
## [59] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [61] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [63] No_antibiotics No_antibiotics
## [65] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [67] No_antibiotics Broad_spectrum_antibiotics
## [69] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
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## [73] Narrow_spectrum_antibiotics No_antibiotics
## [75] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [77] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [79] No_antibiotics Narrow_spectrum_antibiotics
## [81] Broad_spectrum_antibiotics No_antibiotics
## [83] No_antibiotics Narrow_spectrum_antibiotics
## [85] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [87] No_antibiotics No_antibiotics
## [89] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [91] Broad_spectrum_antibiotics No_antibiotics
## [93] No_antibiotics No_antibiotics
## [95] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [97] Narrow_spectrum_antibiotics No_antibiotics
## [99] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [101] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [103] No_antibiotics Narrow_spectrum_antibiotics
## [105] No_antibiotics No_antibiotics
## [107] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [109] No_antibiotics Broad_spectrum_antibiotics
## [111] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [113] No_antibiotics Narrow_spectrum_antibiotics
## [115] No_antibiotics Narrow_spectrum_antibiotics
## [117] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [119] Narrow_spectrum_antibiotics No_antibiotics
## [121] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## 3 Levels: Broad_spectrum_antibiotics ... No_antibioticssample_data(ps0)$randomization_arm <- factor(sample_data(ps0)$randomization_arm, levels = c("No_antibiotics", "Narrow_spectrum_antibiotics", "Broad_spectrum_antibiotics"))
sample_data(ps0)$randomization_arm## [1] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [3] No_antibiotics Broad_spectrum_antibiotics
## [5] No_antibiotics No_antibiotics
## [7] No_antibiotics Narrow_spectrum_antibiotics
## [9] No_antibiotics Broad_spectrum_antibiotics
## [11] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [13] Narrow_spectrum_antibiotics No_antibiotics
## [15] Narrow_spectrum_antibiotics No_antibiotics
## [17] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [19] No_antibiotics Broad_spectrum_antibiotics
## [21] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [23] Broad_spectrum_antibiotics No_antibiotics
## [25] Narrow_spectrum_antibiotics No_antibiotics
## [27] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [29] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [31] Narrow_spectrum_antibiotics No_antibiotics
## [33] No_antibiotics Broad_spectrum_antibiotics
## [35] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [37] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [39] No_antibiotics No_antibiotics
## [41] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [43] Broad_spectrum_antibiotics No_antibiotics
## [45] No_antibiotics No_antibiotics
## [47] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [49] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [51] No_antibiotics Broad_spectrum_antibiotics
## [53] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [55] No_antibiotics No_antibiotics
## [57] Narrow_spectrum_antibiotics No_antibiotics
## [59] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [61] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [63] No_antibiotics No_antibiotics
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## [67] No_antibiotics Broad_spectrum_antibiotics
## [69] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [71] Broad_spectrum_antibiotics No_antibiotics
## [73] Narrow_spectrum_antibiotics No_antibiotics
## [75] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [77] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [79] No_antibiotics Narrow_spectrum_antibiotics
## [81] Broad_spectrum_antibiotics No_antibiotics
## [83] No_antibiotics Narrow_spectrum_antibiotics
## [85] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [87] No_antibiotics No_antibiotics
## [89] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [91] Broad_spectrum_antibiotics No_antibiotics
## [93] No_antibiotics No_antibiotics
## [95] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [97] Narrow_spectrum_antibiotics No_antibiotics
## [99] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## [101] Narrow_spectrum_antibiotics Narrow_spectrum_antibiotics
## [103] No_antibiotics Narrow_spectrum_antibiotics
## [105] No_antibiotics No_antibiotics
## [107] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [109] No_antibiotics Broad_spectrum_antibiotics
## [111] Broad_spectrum_antibiotics Narrow_spectrum_antibiotics
## [113] No_antibiotics Narrow_spectrum_antibiotics
## [115] No_antibiotics Narrow_spectrum_antibiotics
## [117] Broad_spectrum_antibiotics Broad_spectrum_antibiotics
## [119] Narrow_spectrum_antibiotics No_antibiotics
## [121] Narrow_spectrum_antibiotics Broad_spectrum_antibiotics
## 3 Levels: No_antibiotics ... Broad_spectrum_antibioticssample_data(ps0)$randomization_arm <- factor(sample_data(ps0)$randomization_arm, labels = c("No antibiotics", "Narrow spectrum antibiotics", "Broad spectrum antibiotics"))
sample_data(ps0)$randomization_arm## [1] Narrow spectrum antibiotics Broad spectrum antibiotics
## [3] No antibiotics Broad spectrum antibiotics
## [5] No antibiotics No antibiotics
## [7] No antibiotics Narrow spectrum antibiotics
## [9] No antibiotics Broad spectrum antibiotics
## [11] Broad spectrum antibiotics Narrow spectrum antibiotics
## [13] Narrow spectrum antibiotics No antibiotics
## [15] Narrow spectrum antibiotics No antibiotics
## [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [19] No antibiotics Broad spectrum antibiotics
## [21] Broad spectrum antibiotics Narrow spectrum antibiotics
## [23] Broad spectrum antibiotics No antibiotics
## [25] Narrow spectrum antibiotics No antibiotics
## [27] Narrow spectrum antibiotics Broad spectrum antibiotics
## [29] Broad spectrum antibiotics Narrow spectrum antibiotics
## [31] Narrow spectrum antibiotics No antibiotics
## [33] No antibiotics Broad spectrum antibiotics
## [35] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [37] Broad spectrum antibiotics Broad spectrum antibiotics
## [39] No antibiotics No antibiotics
## [41] Narrow spectrum antibiotics Broad spectrum antibiotics
## [43] Broad spectrum antibiotics No antibiotics
## [45] No antibiotics No antibiotics
## [47] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [49] Narrow spectrum antibiotics Broad spectrum antibiotics
## [51] No antibiotics Broad spectrum antibiotics
## [53] Narrow spectrum antibiotics Broad spectrum antibiotics
## [55] No antibiotics No antibiotics
## [57] Narrow spectrum antibiotics No antibiotics
## [59] Broad spectrum antibiotics Narrow spectrum antibiotics
## [61] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [63] No antibiotics No antibiotics
## [65] Broad spectrum antibiotics Narrow spectrum antibiotics
## [67] No antibiotics Broad spectrum antibiotics
## [69] Broad spectrum antibiotics Narrow spectrum antibiotics
## [71] Broad spectrum antibiotics No antibiotics
## [73] Narrow spectrum antibiotics No antibiotics
## [75] Narrow spectrum antibiotics Broad spectrum antibiotics
## [77] Broad spectrum antibiotics Narrow spectrum antibiotics
## [79] No antibiotics Narrow spectrum antibiotics
## [81] Broad spectrum antibiotics No antibiotics
## [83] No antibiotics Narrow spectrum antibiotics
## [85] Broad spectrum antibiotics Broad spectrum antibiotics
## [87] No antibiotics No antibiotics
## [89] Narrow spectrum antibiotics Broad spectrum antibiotics
## [91] Broad spectrum antibiotics No antibiotics
## [93] No antibiotics No antibiotics
## [95] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [97] Narrow spectrum antibiotics No antibiotics
## [99] Narrow spectrum antibiotics Broad spectrum antibiotics
## [101] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [103] No antibiotics Narrow spectrum antibiotics
## [105] No antibiotics No antibiotics
## [107] Broad spectrum antibiotics Narrow spectrum antibiotics
## [109] No antibiotics Broad spectrum antibiotics
## [111] Broad spectrum antibiotics Narrow spectrum antibiotics
## [113] No antibiotics Narrow spectrum antibiotics
## [115] No antibiotics Narrow spectrum antibiotics
## [117] Broad spectrum antibiotics Broad spectrum antibiotics
## [119] Narrow spectrum antibiotics No antibiotics
## [121] Narrow spectrum antibiotics Broad spectrum antibiotics
## 3 Levels: No antibiotics ... Broad spectrum antibioticsSample assessment
# Format a data table to combine sample summary data with sample variable data
ss <- sample_sums(ps0)
sd <- as.data.frame(sample_data(ps0)) # useful to coerce the phyloseq object into an R data frame
ss.df <- merge(sd, data.frame("ASV" = ss), by ="row.names") # merge ss with sd by row names. Rename ss to ASVs in the new data frame
# Plot the data by the treatment variable
y = 100 # Set a threshold for the minimum number of acceptable reads. Can start as a guess
x = "randomization_arm" # Set the x-axis variable you want to examine
label = "Description" # This is the label you want to overlay on the points that are below threshold y. Should be something sample specific
# Plot
p.ss.boxplot<- ggplot(ss.df, aes_string(x, y = "ASV")) + # x is what you assigned it above
geom_boxplot(outlier.colour="NA", aes(group = randomization_arm)) +
scale_y_log10() +
geom_hline(yintercept = y, lty = 2) + # Draws a dashed line across the threshold you set above as y
geom_jitter(alpha = 0.6, width = 0.15, size = 3) +
labs(y = "ASV (log10)") +
facet_grid(~day) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
theme(legend.position = "NULL") +
theme(axis.title.x = element_blank())
p.ss.boxplot
##Outlier sample removal
# Remove samples with fewer than y ASV
# Save samples that fell below the selected threshold
ps.failed <- prune_samples(sample_sums(ps0) < y, ps0)
nsamples(ps.failed)## [1] 22# Create a table of failed sample information
failed.samples <- data.frame(sample_names(ps.failed), sample_data(ps.failed)$sample_ID, sample_data(ps.failed)$tube_ID)
write.table(failed.samples, file = "./results/failed_samples.txt", sep = "\t")
# Remove samples with fewer than y ASV
ps0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1936 taxa and 122 samples ]
## sample_data() Sample Data: [ 122 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]ps0 <- prune_samples(sample_sums(ps0) >=y, ps0)
ps0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1936 taxa and 100 samples ]
## sample_data() Sample Data: [ 100 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]min(sample_sums(ps0))## [1] 103Taxon cleaning
# Begin by removing sequences that were not classified as Bacteria or were classified as either mitochondria or chlorplast
ps0 # Check the number of taxa prior to removal## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1936 taxa and 100 samples ]
## sample_data() Sample Data: [ 100 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1936 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1936 tips and 1934 internal nodes ]ps1 <- ps0 %>%
subset_taxa(
Kingdom == "Bacteria" &
Family != "mitochondria" &
Class != "Chloroplast" &
Phylum != "Cyanobacteria/Chloroplast"
)
ps1 # Confirm that the taxa were removed## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 100 samples ]
## sample_data() Sample Data: [ 100 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]Data transformations
# Some of these are not used in subsequent analysis, but are coded as a chunk here for convenience
# Transform to Realative abundances
ps1.ra <- transform_sample_counts(ps1, function(OTU) OTU/sum(OTU))Subsetting
# No antibiotics (control)
ps1.con <- subset_samples(ps1, randomization_arm == "No antibiotics")
ps1.con## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.con <- prune_samples(sample_sums(ps1.con) > 0, ps1.con)
ps1.con## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 34 samples ]
## sample_data() Sample Data: [ 34 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.con.log <- transform_sample_counts(ps1.con, function(x) log(1 + x))
# Narrow spectrum antibiotics
ps1.narrow <- subset_samples(ps1, randomization_arm == "Narrow spectrum antibiotics")
ps1.narrow## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 36 samples ]
## sample_data() Sample Data: [ 36 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.narrow <- prune_samples(sample_sums(ps1.narrow) > 0, ps1.narrow)
ps1.narrow## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 36 samples ]
## sample_data() Sample Data: [ 36 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.narrow.log <- transform_sample_counts(ps1.narrow, function(x) log(1 + x))
# Broad spectrum antibiotics
ps1.broad <- subset_samples(ps1, randomization_arm == "Broad spectrum antibiotics")
ps1.broad## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 30 samples ]
## sample_data() Sample Data: [ 30 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.broad <- prune_samples(sample_sums(ps1.broad) > 0, ps1.broad)
ps1.broad## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 30 samples ]
## sample_data() Sample Data: [ 30 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.broad.log <- transform_sample_counts(ps1.broad, function(x) log(1 + x))
# Antibiotics
ps1.abx <- subset_samples(ps1, antibiotics == "Antibiotics")
ps1.abx## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 66 samples ]
## sample_data() Sample Data: [ 66 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.abx <- prune_samples(sample_sums(ps1.abx) > 0, ps1.abx)
ps1.abx## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 66 samples ]
## sample_data() Sample Data: [ 66 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.abx.log <- transform_sample_counts(ps1.abx, function(x) log(1 + x))Community composition plotting
These plots were not included in the final manuscript but remain here as compositional overviews. Interactive plots are also produced.
# Phyla level plots
# Melt to long format (for ggploting)
# Prune out phyla below % in each sample
# Set propotional threshold for taxa to be removed
threshold = 0.03
ps1_phylum <- ps1 %>%
tax_glom(taxrank = "Phylum") %>% # agglomerate at phylum level
transform_sample_counts(function(x) {x/sum(x)} ) %>% # Transform to rel. abundance
psmelt() %>% # Melt to long format
filter(Abundance > threshold) %>% # Filter out low abundance taxa
arrange(Phylum) # Sort data frame alphabetically by phylum
# Convert Sample No to a factor because R is weird sometime
ps1_phylum$patient_ID <- as.factor(ps1_phylum$patient_ID)
p.comm.bar <- ggplot(ps1_phylum, aes(x = patient_ID, y = Abundance, fill = Phylum)) +
geom_bar(stat = "identity", width = 0.9) +
facet_wrap(randomization_arm~day, scales = "free_x") +
ggtitle("Community Composition Over Time and Randomization Arm") +
labs(y = "Relative Abundance") +
theme(legend.position = "bottom") +
theme(axis.text.x = element_blank()) +
theme(axis.title.x = element_blank()) +
scale_fill_brewer(palette = "Dark2")
p.comm.bar
# Draw interactive plot
ggplotly(p.comm.bar)# Agglomerate taxa
glom <- tax_glom(ps1.ra, taxrank = 'Phylum')
# Create dataframe from phyloseq object
dat <- as.tibble(psmelt(glom))
# Set comparisons
my_comparisons.1 <- list( c("No antibiotics", "Narrow spectrum antibiotics"), c("No antibiotics", "Broad spectrum antibiotics"), c("Narrow spectrum antibiotics", "Broad spectrum antibiotics") )
my_comparisons.2 <- list( c("-9", "0"), c("-9", "7"), c("0", "7") )
# Table of mean abundances per Phylum
dat.summary <- dat %>%
group_by(Phylum) %>%
summarise(mean = mean(Abundance)) %>%
arrange(desc(mean))
ggplot(dat.summary, aes(x = fct_reorder(Phylum, mean), y = mean)) +
geom_point() +
coord_flip()
# Reorder
levels(dat$Phylum)## [1] "Actinobacteria" "Bacteroidetes" "Elusimicrobia"
## [4] "Firmicutes" "Fusobacteria" "Lentisphaerae"
## [7] "Proteobacteria" "Spirochaetes" "Synergistetes"
## [10] "Tenericutes" "Verrucomicrobia"dat.reorder <- dat %>%
mutate(Phylum = reorder(Phylum, Abundance, mean))
levels(dat.reorder$Phylum)## [1] "Tenericutes" "Elusimicrobia" "Lentisphaerae"
## [4] "Spirochaetes" "Synergistetes" "Fusobacteria"
## [7] "Actinobacteria" "Verrucomicrobia" "Proteobacteria"
## [10] "Bacteroidetes" "Firmicutes"# Determine how mean abundances of Phyla are altered by treatment
p.boxplot.phylum.1 <- ggboxplot(dat.reorder, x = "randomization_arm", y = "Abundance", outlier.shape = NA) +
geom_jitter(width = 0.2) +
labs(title = "Figure S3", y = "Relative Abundance", x = "") +
coord_cartesian(ylim = c(0, 1.5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(comparisons = my_comparisons.1, label = "p.signif", hide.ns = TRUE) +
facet_grid(day~Phylum)
p.boxplot.phylum.1
# Note: For the figure in the manuscript we manually replaced the asterisks and removed the ns for aesthetic purposes
# Subset to phyla with treatment alteration in mean abundnaces
dat.1 <- filter(dat.reorder, Phylum %in% c("Firmicutes",
"Bacteroidetes",
"Proteobacteria",
"Verrucomicrobia",
"Actinobacteria",
"Fusobacteria"))
levels(dat.1$Phylum)## [1] "Tenericutes" "Elusimicrobia" "Lentisphaerae"
## [4] "Spirochaetes" "Synergistetes" "Fusobacteria"
## [7] "Actinobacteria" "Verrucomicrobia" "Proteobacteria"
## [10] "Bacteroidetes" "Firmicutes"dat.1 <- droplevels(dat.1)
levels(dat.1$Phylum)## [1] "Fusobacteria" "Actinobacteria" "Verrucomicrobia" "Proteobacteria"
## [5] "Bacteroidetes" "Firmicutes"# Boxplot with treatment altered phyla
p.boxplot.phylum.2 <- ggboxplot(dat.1, x = "randomization_arm", y = "Abundance", outlier.shape = NA) +
geom_jitter(width = 0.2) +
labs(title = "Figure S3", y = "Relative Abundance", x = "") +
coord_cartesian(ylim = c(0, 1.5)) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(comparisons = my_comparisons.1, label = "p.format", hide.ns = TRUE) +
facet_grid(day~Phylum)
p.boxplot.phylum.2
# Note: For the figure in the manuscript we manually replaced the asterisks and removed the ns for aesthetic purposes
# Not inlcuded in the final manuscript, but referenced in the text
p.boxplot.phylum.3 <- ggpaired(dat.1, x = "day", y = "Abundance", outlier.shape = NA, id = "patient_ID") +
geom_jitter(width = 0.2) +
labs(y = "Relative Abundance") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(comparisons = my_comparisons.2, hide.ns = FALSE, label = "p.signif") +
facet_grid(randomization_arm~Phylum) +
coord_cartesian(ylim = c(0, 1.5))
p.boxplot.phylum.3
# Reorder for legend consistency
# Reorder
levels(dat.1$Phylum)## [1] "Fusobacteria" "Actinobacteria" "Verrucomicrobia" "Proteobacteria"
## [5] "Bacteroidetes" "Firmicutes"dat.1.reorder <- dat.1 %>%
mutate(Phylum = reorder(Phylum, Abundance, function(x) -mean(x)))
levels(dat.1.reorder$Phylum)## [1] "Firmicutes" "Bacteroidetes" "Proteobacteria" "Verrucomicrobia"
## [5] "Actinobacteria" "Fusobacteria"p.gam.phylum <- ggplot(dat.1.reorder, aes(x = day, y = Abundance, color = Phylum)) +
stat_smooth(method = "gam", formula = y ~ s(x, bs = "cr", k = 3)) +
labs(y = "Relative Abundance", x = "Day") +
scale_x_continuous(breaks = c(-9,0,7)) +
coord_cartesian(ylim = c(0, 1)) +
geom_jitter(size = 3, alpha = 0.4, width = 1.5) +
facet_grid(~randomization_arm)
p.gam.phylum
##Alpha diversity
# Diversity calculations
diversity <- global(ps1)
head(diversity)## richness_0 richness_20 richness_50 richness_80
## 20179.VanessaHarris 129 129 129 129
## 20183.VanessaHarris 107 107 107 107
## 20184.VanessaHarris 122 122 122 122
## 20185.VanessaHarris 167 167 167 167
## 20190.VanessaHarris 178 178 178 178
## 20191.VanessaHarris 101 101 101 101
## observed diversities_inverse_simpson
## 20179.VanessaHarris 129 25.051833
## 20183.VanessaHarris 107 11.052659
## 20184.VanessaHarris 122 21.067032
## 20185.VanessaHarris 167 11.979071
## 20190.VanessaHarris 178 7.402668
## 20191.VanessaHarris 101 29.246391
## diversities_gini_simpson diversities_shannon
## 20179.VanessaHarris 0.9600828 3.841603
## 20183.VanessaHarris 0.9095240 3.189909
## 20184.VanessaHarris 0.9525325 3.635530
## 20185.VanessaHarris 0.9165211 3.560559
## 20190.VanessaHarris 0.8649136 3.296459
## 20191.VanessaHarris 0.9658077 3.851619
## diversities_fisher diversities_coverage
## 20179.VanessaHarris 18.39179 9
## 20183.VanessaHarris 13.28609 4
## 20184.VanessaHarris 17.10378 7
## 20185.VanessaHarris 21.72897 6
## 20190.VanessaHarris 23.54276 4
## 20191.VanessaHarris 17.34501 11
## evenness_camargo evenness_pielou evenness_simpson
## 20179.VanessaHarris 0.02411088 0.7904838 0.015775713
## 20183.VanessaHarris 0.01386747 0.6826504 0.006960113
## 20184.VanessaHarris 0.01954583 0.7567682 0.013266393
## 20185.VanessaHarris 0.02284719 0.6956942 0.007543496
## 20190.VanessaHarris 0.02084059 0.6361630 0.004661630
## 20191.VanessaHarris 0.02287182 0.8345651 0.018417123
## evenness_evar evenness_bulla dominance_dbp
## 20179.VanessaHarris 0.3119453 0.07638109 0.1067287
## 20183.VanessaHarris 0.2166349 0.05688918 0.2324156
## 20184.VanessaHarris 0.2539446 0.06913749 0.1180977
## 20185.VanessaHarris 0.2327965 0.08566605 0.2434588
## 20190.VanessaHarris 0.2584905 0.09132655 0.3468711
## 20191.VanessaHarris 0.3542731 0.06214156 0.1007699
## dominance_dmn dominance_absolute dominance_relative
## 20179.VanessaHarris 0.2032787 2181 0.1067287
## 20183.VanessaHarris 0.3572660 9708 0.2324156
## 20184.VanessaHarris 0.1924694 2528 0.1180977
## 20185.VanessaHarris 0.3570235 11510 0.2434588
## 20190.VanessaHarris 0.4207092 15681 0.3468711
## 20191.VanessaHarris 0.1656116 589 0.1007699
## dominance_simpson dominance_core_abundance
## 20179.VanessaHarris 0.03991724 0.4406166
## 20183.VanessaHarris 0.09047596 0.2650946
## 20184.VanessaHarris 0.04746753 0.4612725
## 20185.VanessaHarris 0.08347893 0.2748482
## 20190.VanessaHarris 0.13508643 0.4270356
## 20191.VanessaHarris 0.03419225 0.3223268
## dominance_gini rarity_log_modulo_skewness
## 20179.VanessaHarris 0.9758891 2.060662
## 20183.VanessaHarris 0.9861325 2.059361
## 20184.VanessaHarris 0.9804542 2.057755
## 20185.VanessaHarris 0.9771528 2.060823
## 20190.VanessaHarris 0.9791594 2.060939
## 20191.VanessaHarris 0.9771282 2.061423
## rarity_low_abundance rarity_noncore_abundance
## 20179.VanessaHarris 0.05784194 0.2032787
## 20183.VanessaHarris 0.03243955 0.2483361
## 20184.VanessaHarris 0.05713351 0.2874895
## 20185.VanessaHarris 0.07466633 0.1344628
## 20190.VanessaHarris 0.07226757 0.1914969
## 20191.VanessaHarris 0.03695466 0.2249786
## rarity_rare_abundance
## 20179.VanessaHarris 0.2032787
## 20183.VanessaHarris 0.2483361
## 20184.VanessaHarris 0.2874895
## 20185.VanessaHarris 0.1344628
## 20190.VanessaHarris 0.1914969
## 20191.VanessaHarris 0.2249786# Bind sample data to diversity data
sd.1 <- as.data.frame(sample_data(ps1)) # useful to coerce the phyloseq object into an R data frame
ps1.rich <- merge(sd.1, diversity, by ="row.names") # merge sd.1 by row namesmy.comparisons.paired <- list(c("Pre", "0"), c("Pre", "7"), c("0","7"))
p.paired.rich.1 <- ggpaired(ps1.rich, x = "day", y = "richness_0", outlier.shape = NA, id = "patient_ID") +
geom_jitter(width = 0.2) +
labs(y = "Richness") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~randomization_arm) +
stat_compare_means(label = "p.signif", method = "t.test", ref.group = "-9", hide.ns = TRUE) +
theme(axis.text.x = element_blank())
p.paired.sd.1 <- ggpaired(ps1.rich, x = "day", y = "diversities_shannon", outlier.shape = NA, id = "patient_ID") +
geom_jitter(width = 0.2) +
labs(y = "Shannon Diversity") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~randomization_arm) +
stat_compare_means(label = "p.signif", method = "t.test", ref.group = "-9", hide.ns = TRUE)
ggarrange(p.paired.rich.1, p.paired.sd.1, nrow = 2, labels = c("A)", "B)"))
# Alpha diversity comparisons between treatments
p.paired.rich.2 <- ggboxplot(ps1.rich, x = "randomization_arm", y = "richness_0", outlier.shape = NA, id = "patient_ID") +
geom_jitter(width = 0.2) +
labs(y = "Richness") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~day) +
stat_compare_means(label = "p.format", comparisons = my_comparisons.1, hide.ns = TRUE) +
theme(axis.text.x = element_blank())
p.paired.sd.2 <- ggboxplot(ps1.rich, x = "randomization_arm", y = "diversities_shannon", outlier.shape = NA, id = "patient_ID") +
geom_jitter(width = 0.2) +
labs(y = "Shannon Diversity") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
facet_grid(~day) +
stat_compare_means(label = "p.format", comparisons = my_comparisons.1, hide.ns = TRUE)
ggarrange(p.paired.rich.2, p.paired.sd.2, nrow = 2, labels = c("A)", "B)"))
# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the text# These plots were not inlcuded in the final manuscript, but were discussed in the results
p.rich.rota_boost <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "d7_rota_boost_updated", y = "richness_0", outlier.shape = NA) +
geom_jitter(width = 0.2) +
ylim(0,250) +
labs(y = "Richness", title = "Boost") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
facet_grid(~day) +
theme(axis.text.x = element_blank())
p.sd.rota_boost <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "d7_rota_boost_updated", y = "diversities_shannon", outlier.shape = NA) +
geom_jitter(width = 0.2) +
ylim(0,5) +
labs(y = "Shannon diversity") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
facet_grid(~day)
ggarrange(p.rich.rota_boost, p.sd.rota_boost, nrow = 2, labels = c("A)", "B)"))
# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the textAlpha diversity and shedding
Shedding was defined as having one or more stool samples per patient positive for rotavirus shedding.
p.rich.shedding <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "Shedding", y = "richness_0", outlier.shape = NA) +
geom_jitter(width = 0.2) +
ylim(0,250) +
labs(y = "Richness", title = "Shedding") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
facet_grid(~day) +
theme(axis.text.x = element_blank())
p.sd.shedding <- ggboxplot(subset(ps1.rich, randomization_arm == "Narrow spectrum antibiotics"), x = "Shedding", y = "diversities_shannon", outlier.shape = NA) +
geom_jitter(width = 0.2) +
ylim(0,5) +
labs(y = "Shannon diversity") +
theme(axis.title.x = element_blank()) +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
stat_compare_means(label = "p.format", method = "t.test", hide.ns = TRUE) +
#facet_grid(~randomization_arm) +
scale_x_discrete(labels = c("No", "Yes"))
ggarrange(p.rich.shedding, p.sd.shedding, nrow = 2, labels = c("A)", "B)"))
# Note: This figure was not included in the final manuscript, but the analysis and p-values were referenced in the textBeta diversity analysis
# UniFrac
ord.ps1.uni <- ordinate(ps1, method = "PCoA", distance = "unifrac")
# weighted UniFrac
ord.ps1.wuni <- ordinate(ps1, method = "PCoA", distance = "wunifrac")
# PCoA plot - Boost
p.ord.wuni <- plot_ordination(ps1, ord.ps1.wuni, color = "randomization_arm", shape = "d7_rota_boost_updated") +
geom_point(size=3, alpha = 0.7) +
scale_color_brewer(palette = "Dark2") +
geom_point(colour = "grey50", size = 0.5) +
facet_grid(~ day) +
labs(color = "Treatment", shape = "Day 7 Rotavirus Boosted") +
theme(legend.position = "null") +
geom_hline(yintercept = 0, size = 0.1, lty = 2) +
geom_vline(xintercept = 0, size = 0.1, lty = 2) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank())
p.ord.uni <- plot_ordination(ps1, ord.ps1.uni, color = "randomization_arm", shape = "d7_rota_boost_updated") +
geom_point(size=3, alpha = 0.7) +
scale_color_brewer(palette = "Dark2") +
geom_point(colour = "grey50", size = 0.5) +
facet_grid(~ day) +
labs(color = "Treatment", shape = "Rotavirus Boosted") +
theme(legend.position = "null")+
geom_hline(yintercept = 0, size = 0.1, lty = 2) +
geom_vline(xintercept = 0, size = 0.1, lty = 2) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank())
ggarrange(p.ord.uni, p.ord.wuni, nrow = 2)
# Species ordination for all Phylum
# Note: The final manuscript just showed the top 6 most abundant phylum. Code for this is in the following chunk
plot_ordination(ps1, ord.ps1.wuni, color = "Genus", type = "taxa") +
geom_point(size=2.5) +
geom_point(colour = "grey50", size = 0.25) +
labs(color = "Day 7 Rotavirus Boosted", shape = "Treatment") +
theme(legend.position = "null") +
facet_wrap(~Phylum, ncol = 4) +
geom_hline(yintercept = 0, size = 0.1, lty = 2) +
geom_vline(xintercept = 0, size = 0.1, lty = 2) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank())
## Beta diversity with top 6 Phyla
# Select only top phyla for display purposes
phylum.sum <- tapply(taxa_sums(ps1), tax_table(ps1)[, "Phylum"], sum, na.rm=TRUE)
# Select top 6 most abundant phyla (to correspond with other figrures)
top6phyla <- names(sort(phylum.sum, TRUE))[1:6]
top6phyla## [1] "Firmicutes" "Bacteroidetes" "Proteobacteria" "Verrucomicrobia"
## [5] "Fusobacteria" "Actinobacteria"ps1.top6 <- prune_taxa((tax_table(ps1)[, "Phylum"] %in% top6phyla), ps1)
get_taxa_unique(ps1.top6, "Phylum")## [1] "Bacteroidetes" "Firmicutes" "Verrucomicrobia" "Proteobacteria"
## [5] "Fusobacteria" "Actinobacteria"# UniFrac
ord.ps1.uni.top6 <- ordinate(ps1.top6, method = "PCoA", distance = "unifrac")
# weighted UniFrac
ord.ps1.wuni.top6 <- ordinate(ps1.top6, method = "PCoA", distance = "wunifrac")
p.ord.species <- plot_ordination(ps1.top6, ord.ps1.wuni, color = "Genus", type = "taxa") +
geom_point(size=2.5) +
geom_point(colour = "grey50", size = 0.25) +
labs(color = "Day 7 Rotavirus Boosted", shape = "Treatment") +
theme(legend.position = "null") +
facet_wrap(~Phylum, ncol = 6) +
geom_hline(yintercept = 0, size = 0.1, lty = 2) +
geom_vline(xintercept = 0, size = 0.1, lty = 2) +
theme(panel.grid.major = element_blank()) +
theme(panel.grid.minor = element_blank())
p.ord.species
ggarrange(p.ord.uni, p.ord.wuni, p.ord.species, nrow = 3, labels = c("A)", "B)", "C)"))
# Note: p.ord.species was re-ordered in Keynote because SH couldn't figure out a simple way to reorder the phyla in PhyloSeq. He is sure there is a more sophisticated way to do this though :)Premanova significance testing (ADONIS)
# Create relevant subsets
ps1.d_9 <- subset_samples(ps1, day == "-9")
ps1.d0 <- subset_samples(ps1, day == "0")
ps1.d7 <- subset_samples(ps1, day == "7")
# Day -9
ps1_otu_table.d_9 <- as.data.frame(otu_table(ps1.d_9))
sd.df.d_9 <- as.data.frame(sample_data(ps1.d_9))
ps1_otu_table.d_9$randomization_arm <- sd.df.d_9$randomization_arm
kable(pairwise.adonis(ps1_otu_table.d_9[,1:1588], ps1_otu_table.d_9$randomization_arm), format = "pandoc", caption = "Day -9")| pairs | total.DF | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|
| No antibiotics vs Narrow spectrum antibiotics | 16 | 0.6982874 | 0.0444818 | 0.911 | 1 | |
| No antibiotics vs Broad spectrum antibiotics | 15 | 1.0235432 | 0.0681293 | 0.396 | 1 | |
| Narrow spectrum antibiotics vs Broad spectrum antibiotics | 14 | 0.7760274 | 0.0563317 | 0.840 | 1 |
# Day 0
ps1_otu_table.d0 <- as.data.frame(otu_table(ps1.d0))
sd.df.d0 <- as.data.frame(sample_data(ps1.d0))
ps1_otu_table.d0$randomization_arm <- sd.df.d0$randomization_arm
kable(pairwise.adonis(ps1_otu_table.d0[,1:1588], ps1_otu_table.d0$randomization_arm), format = "pandoc", caption = "Day 0")| pairs | total.DF | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|
| Narrow spectrum antibiotics vs Broad spectrum antibiotics | 22 | 3.115567 | 0.1291932 | 0.001 | 0.003 | * |
| Narrow spectrum antibiotics vs No antibiotics | 23 | 5.332591 | 0.1951001 | 0.001 | 0.003 | * |
| Broad spectrum antibiotics vs No antibiotics | 20 | 3.449463 | 0.1536546 | 0.001 | 0.003 | * |
# Day 7
ps1_otu_table.d7 <- as.data.frame(otu_table(ps1.d7))
sd.df.d7 <- as.data.frame(sample_data(ps1.d7))
ps1_otu_table.d7$randomization_arm <- sd.df.d7$randomization_arm
kable(pairwise.adonis(ps1_otu_table.d7[,1:1588], ps1_otu_table.d7$randomization_arm), format = "pandoc", caption = "Day 7")| pairs | total.DF | F.Model | R2 | p.value | p.adjusted | sig |
|---|---|---|---|---|---|---|
| Narrow spectrum antibiotics vs Broad spectrum antibiotics | 27 | 1.270326 | 0.0465827 | 0.074 | 0.222 | |
| Narrow spectrum antibiotics vs No antibiotics | 28 | 1.570740 | 0.0549772 | 0.031 | 0.093 | |
| Broad spectrum antibiotics vs No antibiotics | 26 | 1.716849 | 0.0642609 | 0.002 | 0.006 | * |
| #Differential Abundance Testing |
Pairwise comparisons across treatments at each sampling time
- Control Vs. narrow x’s 3 time points (-9, 0, 7)
- Control vs. Broad x’s 3 time points (-9, 0, 7)
- Narrow vs. Broad x’s 3 time points (-9, 0, 7) Total: 9 Comparisons
Differential abundance testing preperation
# Set alpha
alpha = 0.05
# Calculate geometric means prior to estimate size factors
# This is required for data sets with lots of zeros
gm_mean = function(x, na.rm=TRUE){
exp(sum(log(x[x > 0]), na.rm=na.rm) / length(x))
}
# Generate subsets
# Pairwise treatment groups
ps1.CvN <- subset_samples(ps1, randomization_arm != "Broad spectrum antibiotics")
sample_data(ps1.CvN)$randomization_arm## [1] No antibiotics No antibiotics
## [3] No antibiotics No antibiotics
## [5] Narrow spectrum antibiotics No antibiotics
## [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [9] No antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics
## [13] Narrow spectrum antibiotics No antibiotics
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] No antibiotics Narrow spectrum antibiotics
## [19] No antibiotics No antibiotics
## [21] Narrow spectrum antibiotics No antibiotics
## [23] No antibiotics Narrow spectrum antibiotics
## [25] Narrow spectrum antibiotics No antibiotics
## [27] Narrow spectrum antibiotics No antibiotics
## [29] No antibiotics Narrow spectrum antibiotics
## [31] No antibiotics Narrow spectrum antibiotics
## [33] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [35] Narrow spectrum antibiotics No antibiotics
## [37] Narrow spectrum antibiotics No antibiotics
## [39] Narrow spectrum antibiotics No antibiotics
## [41] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [43] No antibiotics Narrow spectrum antibiotics
## [45] No antibiotics No antibiotics
## [47] Narrow spectrum antibiotics No antibiotics
## [49] No antibiotics No antibiotics
## [51] No antibiotics Narrow spectrum antibiotics
## [53] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [55] No antibiotics Narrow spectrum antibiotics
## [57] Narrow spectrum antibiotics No antibiotics
## [59] Narrow spectrum antibiotics No antibiotics
## [61] No antibiotics Narrow spectrum antibiotics
## [63] No antibiotics Narrow spectrum antibiotics
## [65] Narrow spectrum antibiotics No antibiotics
## [67] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [69] No antibiotics Narrow spectrum antibiotics
## Levels: No antibiotics Narrow spectrum antibioticsps1.CvB <- subset_samples(ps1, randomization_arm != "Narrow spectrum antibiotics")
sample_data(ps1.CvB)$randomization_arm## [1] No antibiotics No antibiotics
## [3] No antibiotics No antibiotics
## [5] No antibiotics Broad spectrum antibiotics
## [7] No antibiotics Broad spectrum antibiotics
## [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics No antibiotics
## [13] Broad spectrum antibiotics Broad spectrum antibiotics
## [15] No antibiotics Broad spectrum antibiotics
## [17] Broad spectrum antibiotics Broad spectrum antibiotics
## [19] No antibiotics No antibiotics
## [21] Broad spectrum antibiotics No antibiotics
## [23] No antibiotics No antibiotics
## [25] Broad spectrum antibiotics Broad spectrum antibiotics
## [27] No antibiotics No antibiotics
## [29] No antibiotics Broad spectrum antibiotics
## [31] Broad spectrum antibiotics No antibiotics
## [33] Broad spectrum antibiotics Broad spectrum antibiotics
## [35] Broad spectrum antibiotics No antibiotics
## [37] No antibiotics Broad spectrum antibiotics
## [39] Broad spectrum antibiotics No antibiotics
## [41] Broad spectrum antibiotics No antibiotics
## [43] No antibiotics Broad spectrum antibiotics
## [45] No antibiotics No antibiotics
## [47] Broad spectrum antibiotics Broad spectrum antibiotics
## [49] No antibiotics No antibiotics
## [51] No antibiotics Broad spectrum antibiotics
## [53] No antibiotics No antibiotics
## [55] No antibiotics Broad spectrum antibiotics
## [57] No antibiotics Broad spectrum antibiotics
## [59] Broad spectrum antibiotics No antibiotics
## [61] Broad spectrum antibiotics Broad spectrum antibiotics
## [63] No antibiotics Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibioticsps1.NvB <- subset_samples(ps1, randomization_arm != "No antibiotics")
sample_data(ps1.NvB)$randomization_arm## [1] Narrow spectrum antibiotics Broad spectrum antibiotics
## [3] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [7] Broad spectrum antibiotics Broad spectrum antibiotics
## [9] Broad spectrum antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics
## [13] Broad spectrum antibiotics Narrow spectrum antibiotics
## [15] Broad spectrum antibiotics Narrow spectrum antibiotics
## [17] Broad spectrum antibiotics Broad spectrum antibiotics
## [19] Narrow spectrum antibiotics Broad spectrum antibiotics
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] Broad spectrum antibiotics Narrow spectrum antibiotics
## [25] Broad spectrum antibiotics Narrow spectrum antibiotics
## [27] Broad spectrum antibiotics Narrow spectrum antibiotics
## [29] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [31] Broad spectrum antibiotics Narrow spectrum antibiotics
## [33] Broad spectrum antibiotics Broad spectrum antibiotics
## [35] Narrow spectrum antibiotics Broad spectrum antibiotics
## [37] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [39] Broad spectrum antibiotics Broad spectrum antibiotics
## [41] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [43] Broad spectrum antibiotics Narrow spectrum antibiotics
## [45] Broad spectrum antibiotics Broad spectrum antibiotics
## [47] Broad spectrum antibiotics Narrow spectrum antibiotics
## [49] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [51] Narrow spectrum antibiotics Broad spectrum antibiotics
## [53] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [55] Broad spectrum antibiotics Narrow spectrum antibiotics
## [57] Broad spectrum antibiotics Broad spectrum antibiotics
## [59] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [61] Narrow spectrum antibiotics Broad spectrum antibiotics
## [63] Broad spectrum antibiotics Narrow spectrum antibiotics
## [65] Narrow spectrum antibiotics Broad spectrum antibiotics
## Levels: Narrow spectrum antibiotics Broad spectrum antibiotics# Day subsets
ps1.CvN## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 70 samples ]
## sample_data() Sample Data: [ 70 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]ps1.CvN_9 <- subset_samples(ps1.CvN, day == "-9")
ps1.CvN_9## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 17 samples ]
## sample_data() Sample Data: [ 17 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvN_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ps1.CvN.0 <- subset_samples(ps1.CvN, day == "0")
ps1.CvN.0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 24 samples ]
## sample_data() Sample Data: [ 24 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvN.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ps1.CvN.7 <- subset_samples(ps1.CvN, day == "7")
ps1.CvN.7## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 29 samples ]
## sample_data() Sample Data: [ 29 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvN.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7# Control vs Broad by day
ps1.CvB_9 <- subset_samples(ps1.CvB, day == "-9")
ps1.CvB_9## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 16 samples ]
## sample_data() Sample Data: [ 16 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvB_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ps1.CvB.0 <- subset_samples(ps1.CvB, day == "0")
ps1.CvB.0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 21 samples ]
## sample_data() Sample Data: [ 21 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvB.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ps1.CvB.7 <- subset_samples(ps1.CvB, day == "7")
ps1.CvB.7## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 27 samples ]
## sample_data() Sample Data: [ 27 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.CvB.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7# Narrow vs Broad by day
ps1.NvB_9 <- subset_samples(ps1.NvB, day == "-9")
ps1.NvB_9## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 15 samples ]
## sample_data() Sample Data: [ 15 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.NvB_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ps1.NvB.0 <- subset_samples(ps1.NvB, day == "0")
ps1.NvB.0## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 23 samples ]
## sample_data() Sample Data: [ 23 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.NvB.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ps1.NvB.7 <- subset_samples(ps1.NvB, day == "7")
ps1.NvB.7## phyloseq-class experiment-level object
## otu_table() OTU Table: [ 1588 taxa and 28 samples ]
## sample_data() Sample Data: [ 28 samples by 120 sample variables ]
## tax_table() Taxonomy Table: [ 1588 taxa by 7 taxonomic ranks ]
## phy_tree() Phylogenetic Tree: [ 1588 tips and 1586 internal nodes ]sample_data(ps1.NvB.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7Control vs Narrow Spectrum Day -9
# Differential Abundance Testing
sample_data(ps1.CvN_9)$randomization_arm## [1] No antibiotics No antibiotics
## [3] No antibiotics No antibiotics
## [5] Narrow spectrum antibiotics No antibiotics
## [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [9] No antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics
## [13] Narrow spectrum antibiotics No antibiotics
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] No antibiotics
## Levels: No antibiotics Narrow spectrum antibioticssample_data(ps1.CvN_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ds.NoAbx.Narrow.d_9 <- phyloseq_to_deseq2(ps1.CvN_9, ~randomization_arm)
geoMeans.ds.NoAbx.Narrow.d_9 <- apply(counts(ds.NoAbx.Narrow.d_9), 1, gm_mean)
ds.NoAbx.Narrow.d_9 <- estimateSizeFactors(ds.NoAbx.Narrow.d_9, geoMeans = geoMeans.ds.NoAbx.Narrow.d_9)
dds.NoAbx.Narrow.d_9 <- DESeq(ds.NoAbx.Narrow.d_9, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Narrow.d_9 = results(dds.NoAbx.Narrow.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d_9 = res.dds.NoAbx.Narrow.d_9[which(res.dds.NoAbx.Narrow.d_9$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d_9 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d_9, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d_9), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d_9)##
## out of 436 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4, 0.92%
## LFC < 0 (down) : 2, 0.46%
## outliers [1] : 0, 0%
## low counts [2] : 216, 50%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Narrow.d_9)## baseMean
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 6.380570
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 19.804627
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 14.018301
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 25.176158
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 8.690063
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 13.950333
## log2FoldChange
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 21.25830
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 22.82752
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 22.35159
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 22.76903
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -22.07018
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT -20.82741
## lfcSE
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 2.972809
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.970430
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.970899
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.970189
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 2.981681
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.980680
## stat
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 7.150914
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 7.684921
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 7.523511
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 7.665852
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -7.401924
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT -6.987471
## pvalue
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 8.620211e-13
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.530915e-14
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 5.332451e-14
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 1.776479e-14
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.342258e-13
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.798857e-12
## padj
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 7.516824e-11
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 3.872725e-12
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 7.749829e-12
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 3.872725e-12
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.463061e-11
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT 2.033836e-10
## Kingdom
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteria
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteria
## Phylum
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Firmicutes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidetes
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteroidetes
## Class
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Negativicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacilli
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidia
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidia
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteroidia
## Order
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Selenomonadales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidales
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Bacteroidales
## Family
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Veillonellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidaceae
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Porphyromonadaceae
## Genus
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Dialister
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillus
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroides
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroides
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT Barnesiella
## Species
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG <NA>
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT <NA>
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT ruminis
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT <NA>
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGCACGCCAAGTCAGCGGTGAAATTTCCGGGCTCAACCCGGAGTGTGCCGTTGAAACTGGCGAGCTAGAGTACACAAGAGGCAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACCCCGATTGCGAAGGCAGCCTGCTAGGGTGAAACAGACGCTGAGGCACGAAAGCGTGGGT <NA>write.table(sigtab_dds.dds.NoAbx.Narrow.d_9, file="./results/deseq_d_9_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d_9 = data.table(as(results(dds.NoAbx.Narrow.d_9, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.CvN_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d_9, "OTU")
resdt.dds.NoAbx.Narrow.d_9 <- taxdt.dds.d_9.com[resdt.dds.NoAbx.Narrow.d_9]
resdt.dds.NoAbx.Narrow.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.4588114 -2.302210
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.2117591 -1.473163
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 0.000000
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.0000000 NA
## lfcSE stat pvalue padj
## 1: 3.024684 -0.7611405 0.4465731 0.9612434
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: 3.042592 -0.4841803 0.6282579 0.9612434
## 5: NA NA NA NA
## ---
## 1584: 0.000000 0.0000000 1.0000000 NA
## 1585: 0.000000 0.0000000 1.0000000 NA
## 1586: 0.000000 0.0000000 1.0000000 NA
## 1587: NA NA NA NA
## 1588: NA NA NA NAresdt.dds.NoAbx.Narrow.d_9[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d_9[!is.na(Significant)]## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTTTTCTTGAGTGCTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACAGTAACTGACGTTGAGGCACGAAAGCGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 432: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTCAACTGGACGATAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 433: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 434: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 435: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 436: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 432: Bacteria Firmicutes Clostridia Clostridiales
## 433: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 434: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 435: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 436: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean
## 1: Ruminococcaceae <NA> <NA> 0.4588114
## 2: Rikenellaceae <NA> <NA> 0.2117591
## 3: Ruminococcaceae Faecalibacterium <NA> 0.2566115
## 4: Ruminococcaceae Faecalibacterium prausnitzii 1725.8062256
## 5: Ruminococcaceae Faecalibacterium <NA> 6.8274327
## ---
## 432: Peptostreptococcaceae Terrisporobacter <NA> 0.9902423
## 433: Rikenellaceae Alistipes <NA> 27.0876943
## 434: Rikenellaceae Alistipes putredinis 199.1970531
## 435: Verrucomicrobiaceae Akkermansia muciniphila 154.5876656
## 436: Bacteroidaceae Bacteroides <NA> 8.6900633
## log2FoldChange lfcSE stat pvalue padj
## 1: -2.3022098 3.024684 -0.7611405 4.465731e-01 9.612434e-01
## 2: -1.4731633 3.042592 -0.4841803 6.282579e-01 9.612434e-01
## 3: 1.6990947 3.037164 0.5594347 5.758651e-01 9.612434e-01
## 4: 0.9345633 1.158837 0.8064665 4.199739e-01 9.612434e-01
## 5: 6.3025439 2.972589 2.1202204 3.398746e-02 9.612434e-01
## ---
## 432: -3.2987529 3.002899 -1.0985228 2.719763e-01 9.612434e-01
## 433: 1.7229974 2.057375 0.8374737 4.023263e-01 9.612434e-01
## 434: -0.8036767 1.245101 -0.6454710 5.186220e-01 9.612434e-01
## 435: -3.1964282 1.982376 -1.6124226 1.068700e-01 9.612434e-01
## 436: -22.0701779 2.981681 -7.4019235 1.342258e-13 1.463061e-11
## Significant
## 1: FALSE
## 2: FALSE
## 3: FALSE
## 4: FALSE
## 5: FALSE
## ---
## 432: FALSE
## 433: FALSE
## 434: FALSE
## 435: FALSE
## 436: TRUEresdt.dds.NoAbx.Narrow.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.4588114 -2.302210
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.2117591 -1.473163
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 0.000000
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.0000000 NA
## lfcSE stat pvalue padj Significant
## 1: 3.024684 -0.7611405 0.4465731 0.9612434 FALSE
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: 3.042592 -0.4841803 0.6282579 0.9612434 FALSE
## 5: NA NA NA NA NA
## ---
## 1584: 0.000000 0.0000000 1.0000000 NA NA
## 1585: 0.000000 0.0000000 1.0000000 NA NA
## 1586: 0.000000 0.0000000 1.0000000 NA NA
## 1587: NA NA NA NA NA
## 1588: NA NA NA NA NAvolcano.NoAbx.Narrow.d_9 = ggplot(
data = resdt.dds.NoAbx.Narrow.d_9[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Narrow.d_9[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 No Antibiotics vs Narrow Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d_9
summary(res.dds.NoAbx.Narrow.d_9)##
## out of 436 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4, 0.92%
## LFC < 0 (down) : 2, 0.46%
## outliers [1] : 0, 0%
## low counts [2] : 216, 50%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Narrow.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Narrow.d_9)# Differential Abundance Testing
sample_data(ps1.CvN.0)$randomization_arm## [1] Narrow spectrum antibiotics No antibiotics
## [3] No antibiotics No antibiotics
## [5] No antibiotics Narrow spectrum antibiotics
## [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [9] No antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics No antibiotics
## [13] Narrow spectrum antibiotics No antibiotics
## [15] No antibiotics Narrow spectrum antibiotics
## [17] No antibiotics Narrow spectrum antibiotics
## [19] Narrow spectrum antibiotics No antibiotics
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] No antibiotics Narrow spectrum antibiotics
## Levels: No antibiotics Narrow spectrum antibioticssample_data(ps1.CvN.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ds.NoAbx.Narrow.d0 <- phyloseq_to_deseq2(ps1.CvN.0, ~randomization_arm)
geoMeans.ds.NoAbx.Narrow.d0 <- apply(counts(ds.NoAbx.Narrow.d0), 1, gm_mean)
ds.NoAbx.Narrow.d0 <- estimateSizeFactors(ds.NoAbx.Narrow.d0, geoMeans = geoMeans.ds.NoAbx.Narrow.d0)
dds.NoAbx.Narrow.d0 <- DESeq(ds.NoAbx.Narrow.d0, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Narrow.d0 = results(dds.NoAbx.Narrow.d0, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d0 = res.dds.NoAbx.Narrow.d0[which(res.dds.NoAbx.Narrow.d0$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d0 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d0, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d0), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d0)##
## out of 410 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 18, 4.4%
## LFC < 0 (down) : 122, 30%
## outliers [1] : 0, 0%
## low counts [2] : 492, 120%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Narrow.d0)## baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 493.9877
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 712.2997
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 110.1263
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 437.4761
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1716.4159
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 273.5243
## log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -9.226542
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -13.138562
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -6.373276
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -4.208895
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 9.393029
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -7.097995
## lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.703820
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.247507
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.906295
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.690638
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.104618
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.662932
## stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -5.415210
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -10.531855
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -2.192921
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -2.489531
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 8.503419
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -4.268362
## pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.121676e-08
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.160853e-26
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.831304e-02
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.279118e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.840869e-17
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.969133e-05
## padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.740179e-07
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.791202e-24
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 4.710488e-02
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.404743e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 3.460835e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 6.494683e-05
## Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroides
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Escherichia/Shigella
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT <NA>
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitziiwrite.table(sigtab_dds.dds.NoAbx.Narrow.d0, file="./results/deseq_d0_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d0 = data.table(as(results(dds.NoAbx.Narrow.d0, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.CvN.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d0, "OTU")
resdt.dds.NoAbx.Narrow.d0 <- taxdt.dds.d0.com[resdt.dds.NoAbx.Narrow.d0]
resdt.dds.NoAbx.Narrow.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.2373005 1.423492
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 0.000000
## 1588: Prevotellaceae Prevotella bivia 0.4302047 2.036785
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: 3.000450 0.4744264 0.6351959 NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: 0.000000 0.0000000 1.0000000 NA
## 1588: 2.992706 0.6805833 0.4961352 NAresdt.dds.NoAbx.Narrow.d0[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d0[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 184: GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 185: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 186: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 187: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 188: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 184: Bacteria Firmicutes Clostridia Clostridiales
## 185: Bacteria Firmicutes Clostridia Clostridiales
## 186: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 187: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 188: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium prausnitzii 493.987697
## 2: Ruminococcaceae Faecalibacterium prausnitzii 712.299690
## 3: Ruminococcaceae Faecalibacterium prausnitzii 273.524332
## 4: Ruminococcaceae Faecalibacterium prausnitzii 126.784775
## 5: Ruminococcaceae Faecalibacterium prausnitzii 94.794736
## ---
## 184: Peptostreptococcaceae Romboutsia <NA> 36.755315
## 185: Peptostreptococcaceae Intestinibacter bartlettii 8.255784
## 186: Rikenellaceae Alistipes <NA> 8.014145
## 187: Rikenellaceae Alistipes putredinis 72.556359
## 188: Verrucomicrobiaceae Akkermansia muciniphila 982.106534
## log2FoldChange lfcSE stat pvalue padj
## 1: -9.2265418 1.703820 -5.4152102 6.121676e-08 2.740179e-07
## 2: -13.1385617 1.247507 -10.5318546 6.160853e-26 5.791202e-24
## 3: -7.0979951 1.662932 -4.2683624 1.969133e-05 6.494683e-05
## 4: -26.9168699 2.950364 -9.1232385 7.291230e-20 2.284585e-18
## 5: -10.2208721 1.842767 -5.5464820 2.914746e-08 1.369931e-07
## ---
## 184: 0.3299962 1.614617 0.2043805 8.380562e-01 8.380562e-01
## 185: -0.8647764 2.904564 -0.2977302 7.659091e-01 7.783293e-01
## 186: -1.5267737 2.906078 -0.5253726 5.993242e-01 6.156992e-01
## 187: -6.5553995 1.968679 -3.3298463 8.689394e-04 2.042008e-03
## 188: 3.7576177 2.243088 1.6751984 9.389531e-02 1.209063e-01
## Significant
## 1: TRUE
## 2: TRUE
## 3: TRUE
## 4: TRUE
## 5: TRUE
## ---
## 184: FALSE
## 185: FALSE
## 186: FALSE
## 187: TRUE
## 188: FALSEresdt.dds.NoAbx.Narrow.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.2373005 1.423492
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 0.000000
## 1588: Prevotellaceae Prevotella bivia 0.4302047 2.036785
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: 3.000450 0.4744264 0.6351959 NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: 0.000000 0.0000000 1.0000000 NA NA
## 1588: 2.992706 0.6805833 0.4961352 NA NAvolcano.NoAbx.Narrow.d0 = ggplot(
data = resdt.dds.NoAbx.Narrow.d0[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Narrow.d0[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 No Antibiotics vs Narrow Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d0
summary(res.dds.NoAbx.Narrow.d0)##
## out of 410 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 18, 4.4%
## LFC < 0 (down) : 122, 30%
## outliers [1] : 0, 0%
## low counts [2] : 492, 120%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Narrow.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Narrow.d0)nrow(res.dds.NoAbx.Narrow.d0)## [1] 1588Control vs Narrow Spectrum Day 7
# Differential Abundance Testing
sample_data(ps1.CvN.7)$randomization_arm## [1] Narrow spectrum antibiotics No antibiotics
## [3] No antibiotics Narrow spectrum antibiotics
## [5] No antibiotics No antibiotics
## [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [9] No antibiotics Narrow spectrum antibiotics
## [11] No antibiotics No antibiotics
## [13] Narrow spectrum antibiotics No antibiotics
## [15] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [19] No antibiotics Narrow spectrum antibiotics
## [21] No antibiotics Narrow spectrum antibiotics
## [23] No antibiotics Narrow spectrum antibiotics
## [25] Narrow spectrum antibiotics No antibiotics
## [27] Narrow spectrum antibiotics No antibiotics
## [29] No antibiotics
## Levels: No antibiotics Narrow spectrum antibioticssample_data(ps1.CvN.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7ds.NoAbx.Narrow.d7 <- phyloseq_to_deseq2(ps1.CvN.7, ~randomization_arm)
geoMeans.ds.NoAbx.Narrow.d7 <- apply(counts(ds.NoAbx.Narrow.d7), 1, gm_mean)
ds.NoAbx.Narrow.d7 <- estimateSizeFactors(ds.NoAbx.Narrow.d7, geoMeans = geoMeans.ds.NoAbx.Narrow.d7)
dds.NoAbx.Narrow.d7 <- DESeq(ds.NoAbx.Narrow.d7, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Narrow.d7 = results(dds.NoAbx.Narrow.d7, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Narrow.d7 = res.dds.NoAbx.Narrow.d7[which(res.dds.NoAbx.Narrow.d7$padj < alpha), ]
sigtab_dds.dds.NoAbx.Narrow.d7 = cbind(as(sigtab_dds.dds.NoAbx.Narrow.d7, "data.frame"), as(tax_table(ps1.CvN_9)[rownames(sigtab_dds.dds.NoAbx.Narrow.d7), ], "matrix"))
summary(res.dds.NoAbx.Narrow.d7)##
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 6, 1.2%
## LFC < 0 (down) : 7, 1.4%
## outliers [1] : 0, 0%
## low counts [2] : 569, 110%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Narrow.d7)## baseMean
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 22.252930
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 59.863464
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 14.396651
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 6.008561
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 6.705112
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 7.508508
## log2FoldChange
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 7.407523
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 23.888504
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 22.520806
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 21.302374
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG -22.321128
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG -22.677763
## lfcSE
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 2.309413
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 2.938291
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 2.938839
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 2.939843
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 2.938088
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 2.937734
## stat
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 3.207535
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 8.130068
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 7.663164
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 7.246093
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG -7.597162
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG -7.719474
## pvalue
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.338778e-03
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 4.290506e-16
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 1.814076e-14
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 4.289662e-13
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 3.026955e-14
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 1.168111e-14
## padj
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 4.211065e-02
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 3.711288e-14
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 8.966719e-13
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT 1.649137e-11
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG 1.309158e-12
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 6.736106e-13
## Kingdom
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Bacteria
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Bacteria
## Phylum
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Firmicutes
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Bacteroidetes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Firmicutes
## Class
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Clostridia
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Bacteroidia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Clostridia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Clostridia
## Order
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Clostridiales
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Bacteroidales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Clostridiales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Clostridiales
## Family
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Ruminococcaceae
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Prevotellaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG Lachnospiraceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Ruminococcaceae
## Genus
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacterium
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Ruminococcus
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT Prevotella
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Oscillibacter
## Species
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT <NA>
## CCAGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGCCCCTTAAGCGTGTTGTGAAATGCCGCGGCTCAACCGTGGCACTGCAGCGCGAACTGGGGGGCTTGAGTGCACGCAACGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCGGGAGTGCGACTGACGCTGAAGCTCGAAGGTGCGGGT <NA>
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGGAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTCTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAAGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTACTTTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG <NA>write.table(sigtab_dds.dds.NoAbx.Narrow.d7, file="./results/deseq_d7_NoAbx.Narrow.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Narrow.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Narrow.d7 = data.table(as(results(dds.NoAbx.Narrow.d7, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Narrow.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.CvN.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.NoAbx.Narrow.d7, "OTU")
resdt.dds.NoAbx.Narrow.d7 <- taxdt.dds.d7.com[resdt.dds.NoAbx.Narrow.d7]
resdt.dds.NoAbx.Narrow.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 0.000000
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.1590145 -1.974641
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 0.000000
## 1588: Prevotellaceae Prevotella bivia 0.4032097 -2.876681
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: 0.000000 0.0000000 1.0000000 NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: 2.978829 -0.6628915 0.5074001 NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: 0.000000 0.0000000 1.0000000 NA
## 1588: 2.965841 -0.9699377 0.3320776 0.741791resdt.dds.NoAbx.Narrow.d7[, Significant := padj < alpha]
resdt.dds.NoAbx.Narrow.d7[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 342: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 343: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCGGTTTCGTAAGTCGTGTGTGAAAGGCGGGGGCTCAACCCCCGGACTGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 344: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 345: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 346: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 342: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 343: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 344: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 345: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 346: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium prausnitzii 1814.6187149
## 2: Ruminococcaceae Faecalibacterium prausnitzii 1290.2535684
## 3: Ruminococcaceae Faecalibacterium prausnitzii 1032.7416539
## 4: Ruminococcaceae Faecalibacterium prausnitzii 25.0903035
## 5: Ruminococcaceae Faecalibacterium prausnitzii 182.2151655
## ---
## 342: Rikenellaceae Alistipes putredinis 68.1645873
## 343: Verrucomicrobiaceae Akkermansia <NA> 0.6421931
## 344: Verrucomicrobiaceae Akkermansia muciniphila 1374.1188362
## 345: Bacteroidaceae Bacteroides <NA> 2.2815044
## 346: Prevotellaceae Prevotella bivia 0.4032097
## log2FoldChange lfcSE stat pvalue padj Significant
## 1: -1.35245320 1.732849 -0.780479732 0.43510855 0.7675539 FALSE
## 2: -0.84392684 1.637387 -0.515410757 0.60626602 0.8291227 FALSE
## 3: -3.88936387 1.711193 -2.272896025 0.02303245 0.4687780 FALSE
## 4: 2.09170546 2.892648 0.723110986 0.46961170 0.7782087 FALSE
## 5: -2.09010636 1.895218 -1.102831743 0.27010024 0.7417910 FALSE
## ---
## 342: -0.95403735 1.995853 -0.478009710 0.63264329 0.8479897 FALSE
## 343: -0.02107634 2.967394 -0.007102642 0.99433296 0.9943330 FALSE
## 344: 3.11350252 1.943173 1.602277723 0.10909421 0.7417910 FALSE
## 345: -5.10955229 2.944002 -1.735580232 0.08263808 0.7417910 FALSE
## 346: -2.87668094 2.965841 -0.969937680 0.33207756 0.7417910 FALSEresdt.dds.NoAbx.Narrow.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 0.000000
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.1590145 -1.974641
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 0.000000
## 1588: Prevotellaceae Prevotella bivia 0.4032097 -2.876681
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: 0.000000 0.0000000 1.0000000 NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: 2.978829 -0.6628915 0.5074001 NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: 0.000000 0.0000000 1.0000000 NA NA
## 1588: 2.965841 -0.9699377 0.3320776 0.741791 FALSEvolcano.NoAbx.Narrow.d7 = ggplot(
data = resdt.dds.NoAbx.Narrow.d7[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Narrow.d7[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 No Antibiotics vs Narrow Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Narrow.d7
summary(res.dds.NoAbx.Narrow.d7)##
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 6, 1.2%
## LFC < 0 (down) : 7, 1.4%
## outliers [1] : 0, 0%
## low counts [2] : 569, 110%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Narrow.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Narrow.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Narrow.d7)Control vs. Broad Spectrum Antibiotics Day -9
# Differential Abundance Testing
sample_data(ps1.CvB_9)$randomization_arm## [1] No antibiotics No antibiotics
## [3] No antibiotics No antibiotics
## [5] No antibiotics Broad spectrum antibiotics
## [7] No antibiotics Broad spectrum antibiotics
## [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics No antibiotics
## [13] Broad spectrum antibiotics Broad spectrum antibiotics
## [15] No antibiotics Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibioticssample_data(ps1.CvB_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ds.NoAbx.Broad.d_9 <- phyloseq_to_deseq2(ps1.CvB_9, ~randomization_arm)
geoMeans.ds.NoAbx.Broad.d_9 <- apply(counts(ds.NoAbx.Broad.d_9), 1, gm_mean)
ds.NoAbx.Broad.d_9 <- estimateSizeFactors(ds.NoAbx.Broad.d_9, geoMeans = geoMeans.ds.NoAbx.Broad.d_9)
dds.NoAbx.Broad.d_9 <- DESeq(ds.NoAbx.Broad.d_9, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Broad.d_9 = results(dds.NoAbx.Broad.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d_9 = res.dds.NoAbx.Broad.d_9[which(res.dds.NoAbx.Broad.d_9$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d_9 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d_9, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d_9), ], "matrix"))
summary(res.dds.NoAbx.Broad.d_9)##
## out of 439 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 7, 1.6%
## LFC < 0 (down) : 2, 0.46%
## outliers [1] : 0, 0%
## low counts [2] : 237, 54%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Broad.d_9)## baseMean
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 48.46057
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 36.84827
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 107.08180
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 39.38256
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 10.43596
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 29.17452
## log2FoldChange
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT -24.301966
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 8.825884
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 25.355572
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 8.921779
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -21.987913
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 23.539751
## lfcSE
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 3.009187
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 2.606634
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.986930
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 2.635124
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 3.011053
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 2.987729
## stat
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT -8.075925
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 3.385931
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 8.488841
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 3.385715
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT -7.302400
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 7.878811
## pvalue
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 6.696682e-16
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 7.093713e-04
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.087089e-17
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 7.099314e-04
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 2.826789e-13
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 3.305114e-15
## padj
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT 1.469922e-13
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 3.462888e-02
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 9.162320e-15
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG 3.462888e-02
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT 1.836675e-11
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG 4.836484e-13
## Kingdom
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Bacteria
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteria
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Bacteria
## Phylum
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Firmicutes
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidetes
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Firmicutes
## Class
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroidia
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Negativicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Clostridia
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidia
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Erysipelotrichia
## Order
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroidales
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Selenomonadales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Clostridiales
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidales
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Erysipelotrichales
## Family
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroidaceae
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Acidaminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG Ruminococcaceae
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroidaceae
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Erysipelotrichaceae
## Genus
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT Bacteroides
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Phascolarctobacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT Bacteroides
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG Holdemanella
## Species
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGATTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGAAACTGGCAGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTAGACTGTTACTGACACTGATGCTCGAAAGTGTGGGT ovatus
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGTTTTTTAAGTCTGGAGTGAAAATGCGGGGCTCAACCCCGTATGGCTCTGGATACTGGAAGACTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT succinatutens
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT <NA>
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGTAGCCGGGAGGGCAAGTCAGATGTGAAATCCACGGGCTTAACTCGTGAACTGCATTTGAAACTGTTCTTCTTGAGTATCGGAGAGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGACAACTGACGGTGAGGCGCGAAAGCGTGGGG <NA>
## TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT <NA>
## GCGAGCGTTATCCGGAATGATTGGGCGTAAAGGGTGCGTAGGTGGCAGAACAAGTCTGGAGTAAAAGGTATGGGCTCAACCCGTACTGGCTCTGGAAACTGTTCAGCTAGAGAACAGAAGAGGACGGCGGAACTCCATGTGTAGCGGTAAAATGCGTAGATATATGGAAGAACACCGGTGGCGAAGGCGGCCGTCTGGTCTGTTGCTGACACTGAAGCACGAAAGCGTGGGG biformiswrite.table(sigtab_dds.dds.NoAbx.Broad.d_9, file="./results/deseq_d_9_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d_9 = data.table(as(results(dds.NoAbx.Broad.d_9, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.CvB_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d_9, "OTU")
resdt.dds.NoAbx.Broad.d_9 <- taxdt.dds.d_9.com[resdt.dds.NoAbx.Broad.d_9]
resdt.dds.NoAbx.Broad.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.5258314 -2.394897
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.2426914 -1.525241
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.0000000 NA
## lfcSE stat pvalue padj
## 1: 3.050920 -0.7849754 0.4324680 0.9326554
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: 3.070373 -0.4967609 0.6193577 0.9326554
## 5: NA NA NA NA
## ---
## 1584: 0.000000 0.0000000 1.0000000 NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: NA NA NA NA
## 1588: NA NA NA NAresdt.dds.NoAbx.Broad.d_9[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d_9[!is.na(Significant)]## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 435: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 436: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 437: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 438: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## 439: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGACGCTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGGTGTCTTGAGTACAGTAGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTGCTGGACTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 435: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 436: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 437: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 438: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## 439: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean
## 1: Ruminococcaceae <NA> <NA> 0.5258314
## 2: Rikenellaceae <NA> <NA> 0.2426914
## 3: Ruminococcaceae Faecalibacterium <NA> 107.0817997
## 4: Ruminococcaceae Faecalibacterium prausnitzii 1485.4282916
## 5: Ruminococcaceae Faecalibacterium prausnitzii 1749.3431145
## ---
## 435: Rikenellaceae Alistipes <NA> 4.1518365
## 436: Rikenellaceae Alistipes <NA> 33.8081991
## 437: Rikenellaceae Alistipes putredinis 281.3899873
## 438: Verrucomicrobiaceae Akkermansia muciniphila 199.8373307
## 439: Bacteroidaceae Bacteroides <NA> 10.4359558
## log2FoldChange lfcSE stat pvalue padj
## 1: -2.39489729 3.050920 -0.78497541 4.324680e-01 9.326554e-01
## 2: -1.52524141 3.070373 -0.49676091 6.193577e-01 9.326554e-01
## 3: 25.35557225 2.986930 8.48884053 2.087089e-17 9.162320e-15
## 4: 0.46395927 1.095888 0.42336376 6.720299e-01 9.326554e-01
## 5: -0.09913691 1.013482 -0.09781812 9.220767e-01 9.707235e-01
## ---
## 435: 5.63512784 2.992748 1.88292770 5.971017e-02 9.326554e-01
## 436: 2.26652658 2.913005 0.77807157 4.365268e-01 9.326554e-01
## 437: 0.30039331 1.357168 0.22133832 8.248290e-01 9.412854e-01
## 438: -1.60459534 2.347218 -0.68361577 4.942178e-01 9.326554e-01
## 439: -21.98791258 3.011053 -7.30240023 2.826789e-13 1.836675e-11
## Significant
## 1: FALSE
## 2: FALSE
## 3: TRUE
## 4: FALSE
## 5: FALSE
## ---
## 435: FALSE
## 436: FALSE
## 437: FALSE
## 438: FALSE
## 439: TRUEresdt.dds.NoAbx.Broad.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.5258314 -2.394897
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.2426914 -1.525241
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 0.000000
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.0000000 NA
## lfcSE stat pvalue padj Significant
## 1: 3.050920 -0.7849754 0.4324680 0.9326554 FALSE
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: 3.070373 -0.4967609 0.6193577 0.9326554 FALSE
## 5: NA NA NA NA NA
## ---
## 1584: 0.000000 0.0000000 1.0000000 NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: NA NA NA NA NA
## 1588: NA NA NA NA NAvolcano.NoAbx.Broad.d_9 = ggplot(
data = resdt.dds.NoAbx.Broad.d_9[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Broad.d_9[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 No Antibiotics vs Broad Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d_9
summary(res.dds.NoAbx.Broad.d_9)##
## out of 439 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 7, 1.6%
## LFC < 0 (down) : 2, 0.46%
## outliers [1] : 0, 0%
## low counts [2] : 237, 54%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Broad.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Broad.d_9)# Differential Abundance Testing
sample_data(ps1.CvB.0)$randomization_arm## [1] Broad spectrum antibiotics No antibiotics
## [3] No antibiotics Broad spectrum antibiotics
## [5] Broad spectrum antibiotics No antibiotics
## [7] No antibiotics No antibiotics
## [9] Broad spectrum antibiotics No antibiotics
## [11] No antibiotics No antibiotics
## [13] Broad spectrum antibiotics No antibiotics
## [15] Broad spectrum antibiotics Broad spectrum antibiotics
## [17] No antibiotics Broad spectrum antibiotics
## [19] Broad spectrum antibiotics No antibiotics
## [21] Broad spectrum antibiotics
## Levels: No antibiotics Broad spectrum antibioticssample_data(ps1.CvB.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ds.NoAbx.Broad.d0 <- phyloseq_to_deseq2(ps1.CvB.0, ~randomization_arm)
geoMeans.ds.NoAbx.Broad.d0 <- apply(counts(ds.NoAbx.Broad.d0), 1, gm_mean)
ds.NoAbx.Broad.d0 <- estimateSizeFactors(ds.NoAbx.Broad.d0, geoMeans = geoMeans.ds.NoAbx.Broad.d0)
dds.NoAbx.Broad.d0 <- DESeq(ds.NoAbx.Broad.d0, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Broad.d0 = results(dds.NoAbx.Broad.d0, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d0 = res.dds.NoAbx.Broad.d0[which(res.dds.NoAbx.Broad.d0$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d0 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d0, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d0), ], "matrix"))
summary(res.dds.NoAbx.Broad.d0)##
## out of 382 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 9, 2.4%
## LFC < 0 (down) : 77, 20%
## outliers [1] : 0, 0%
## low counts [2] : 455, 119%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Broad.d0)## baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 301.5808
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 451.8549
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 257.5498
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 165.9611
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 150.5424
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 168.1260
## log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -6.474862
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -4.608870
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -5.193611
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -6.902948
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -6.606505
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG -3.872766
## lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.648019
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.303228
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.404298
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.600530
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.916255
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 1.681402
## stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -3.928876
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -3.536503
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -3.698367
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -4.312913
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -2.265407
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG -2.303296
## pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 8.534366e-05
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 4.054615e-04
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.169911e-04
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 1.611174e-05
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.348773e-02
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 2.126220e-02
## padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0004830121
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0018089000
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 0.0010270911
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 0.0001271037
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 0.0497799681
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG 0.0471755171
## Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Bacteria
## Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Firmicutes
## Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Clostridia
## Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Clostridiales
## Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Lachnospiraceae
## Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG Roseburia
## Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT <NA>
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG <NA>write.table(sigtab_dds.dds.NoAbx.Broad.d0, file="./results/deseq_d0_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d0 = data.table(as(results(dds.NoAbx.Broad.d0, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.CvB.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d0, "OTU")
resdt.dds.NoAbx.Broad.d0 <- taxdt.dds.d0.com[resdt.dds.NoAbx.Broad.d0]
resdt.dds.NoAbx.Broad.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0 NA
## 1587: Prevotellaceae Prevotella <NA> 0 0
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: 0 0 1 NA
## 1588: NA NA NA NAresdt.dds.NoAbx.Broad.d0[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d0[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 138: GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 139: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 140: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 141: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 142: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 138: Bacteria Firmicutes Clostridia Clostridiales
## 139: Bacteria Firmicutes Clostridia Clostridiales
## 140: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 141: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 142: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium prausnitzii 301.580788
## 2: Ruminococcaceae Faecalibacterium prausnitzii 451.854908
## 3: Ruminococcaceae Faecalibacterium prausnitzii 165.961121
## 4: Ruminococcaceae Faecalibacterium prausnitzii 85.555734
## 5: Ruminococcaceae Faecalibacterium prausnitzii 57.703750
## ---
## 138: Peptostreptococcaceae Romboutsia <NA> 9.709707
## 139: Peptostreptococcaceae Intestinibacter bartlettii 3.445454
## 140: Rikenellaceae Alistipes <NA> 3.460808
## 141: Rikenellaceae Alistipes putredinis 42.467602
## 142: Verrucomicrobiaceae Akkermansia muciniphila 40.794006
## log2FoldChange lfcSE stat pvalue padj
## 1: -6.474862 1.648019 -3.9288765 8.534366e-05 4.830121e-04
## 2: -4.608870 1.303228 -3.5365033 4.054615e-04 1.808900e-03
## 3: -6.902948 1.600530 -4.3129132 1.611174e-05 1.271037e-04
## 4: -25.076665 2.959964 -8.4719506 2.413175e-17 8.566771e-16
## 5: -8.375351 1.820343 -4.6009750 4.205178e-06 3.732096e-05
## ---
## 138: -2.854650 2.072512 -1.3773866 1.683927e-01 1.882817e-01
## 139: -2.117982 2.931472 -0.7224979 4.699884e-01 4.733216e-01
## 140: -4.309155 2.823670 -1.5260828 1.269892e-01 1.505607e-01
## 141: -7.932753 1.879065 -4.2216482 2.425224e-05 1.812536e-04
## 142: -2.315473 2.036296 -1.1371007 2.554962e-01 2.648209e-01
## Significant
## 1: TRUE
## 2: TRUE
## 3: TRUE
## 4: TRUE
## 5: TRUE
## ---
## 138: FALSE
## 139: FALSE
## 140: FALSE
## 141: TRUE
## 142: FALSEresdt.dds.NoAbx.Broad.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0 NA
## 1587: Prevotellaceae Prevotella <NA> 0 0
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: 0 0 1 NA NA
## 1588: NA NA NA NA NAvolcano.NoAbx.Broad.d0 = ggplot(
data = resdt.dds.NoAbx.Broad.d0[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Broad.d0[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 No Antibiotics vs Broad Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d0
summary(res.dds.NoAbx.Broad.d0)##
## out of 382 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 9, 2.4%
## LFC < 0 (down) : 77, 20%
## outliers [1] : 0, 0%
## low counts [2] : 455, 119%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Broad.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Broad.d0)# Differential Abundance Testing
sample_data(ps1.CvB.7)$randomization_arm## [1] Broad spectrum antibiotics Broad spectrum antibiotics
## [3] No antibiotics No antibiotics
## [5] Broad spectrum antibiotics No antibiotics
## [7] No antibiotics No antibiotics
## [9] Broad spectrum antibiotics Broad spectrum antibiotics
## [11] No antibiotics No antibiotics
## [13] No antibiotics Broad spectrum antibiotics
## [15] Broad spectrum antibiotics No antibiotics
## [17] Broad spectrum antibiotics Broad spectrum antibiotics
## [19] Broad spectrum antibiotics No antibiotics
## [21] No antibiotics Broad spectrum antibiotics
## [23] Broad spectrum antibiotics No antibiotics
## [25] Broad spectrum antibiotics No antibiotics
## [27] No antibiotics
## Levels: No antibiotics Broad spectrum antibioticssample_data(ps1.CvB.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7ds.NoAbx.Broad.d7 <- phyloseq_to_deseq2(ps1.CvB.7, ~randomization_arm)
geoMeans.ds.NoAbx.Broad.d7 <- apply(counts(ds.NoAbx.Broad.d7), 1, gm_mean)
ds.NoAbx.Broad.d7 <- estimateSizeFactors(ds.NoAbx.Broad.d7, geoMeans = geoMeans.ds.NoAbx.Broad.d7)
dds.NoAbx.Broad.d7 <- DESeq(ds.NoAbx.Broad.d7, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.NoAbx.Broad.d7 = results(dds.NoAbx.Broad.d7, cooksCutoff = FALSE)
sigtab_dds.dds.NoAbx.Broad.d7 = res.dds.NoAbx.Broad.d7[which(res.dds.NoAbx.Broad.d7$padj < alpha), ]
sigtab_dds.dds.NoAbx.Broad.d7 = cbind(as(sigtab_dds.dds.NoAbx.Broad.d7, "data.frame"), as(tax_table(ps1.CvB_9)[rownames(sigtab_dds.dds.NoAbx.Broad.d7), ], "matrix"))
summary(res.dds.NoAbx.Broad.d7)##
## out of 480 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 8, 1.7%
## LFC < 0 (down) : 36, 7.5%
## outliers [1] : 0, 0%
## low counts [2] : 615, 128%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.NoAbx.Broad.d7)## baseMean
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 78.35087
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 33.27089
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 14.17571
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 12.43114
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 119.84760
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 26.11912
## log2FoldChange
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -6.319005
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -25.007883
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 22.899554
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -23.677355
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -10.440964
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG -7.057749
## lfcSE
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.061875
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.942847
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 2.939721
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.943814
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.051790
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 2.454841
## stat
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -3.064689
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -8.497853
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 7.789702
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -8.043087
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -5.088710
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG -2.875033
## pvalue
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.178966e-03
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.931298e-17
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 6.716737e-15
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 8.760291e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.605066e-07
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 4.039856e-03
## padj
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 1.357663e-02
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 7.821758e-16
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT 1.088111e-13
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.027382e-14
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.893471e-06
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG 2.423914e-02
## Kingdom
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Bacteria
## Phylum
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Firmicutes
## Class
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Clostridia
## Order
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Clostridiales
## Family
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacteriaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Ruminococcaceae
## Genus
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT Fusobacterium
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG Clostridium_IV
## Species
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT copri
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT <NA>
## ACGAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTATAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT dorei
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG <NA>write.table(sigtab_dds.dds.NoAbx.Broad.d7, file="./results/deseq_d7_NoAbx.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.NoAbx.Broad.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.NoAbx.Broad.d7 = data.table(as(results(dds.NoAbx.Broad.d7, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.NoAbx.Broad.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.CvB.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.NoAbx.Broad.d7, "OTU")
resdt.dds.NoAbx.Broad.d7 <- taxdt.dds.d7.com[resdt.dds.NoAbx.Broad.d7]
resdt.dds.NoAbx.Broad.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.1383900 -1.388132
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.3537685 -2.308552
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: 2.986302 -0.4648330 0.6420510 NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: NA NA NA NA
## 1588: 2.973104 -0.7764788 0.4374663 NAresdt.dds.NoAbx.Broad.d7[, Significant := padj < alpha]
resdt.dds.NoAbx.Broad.d7[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 158: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 159: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTTTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 160: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 161: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 162: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 158: Bacteria Firmicutes Clostridia Clostridiales
## 159: Bacteria Firmicutes Clostridia Clostridiales
## 160: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 161: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 162: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium prausnitzii 1153.752813
## 2: Ruminococcaceae Faecalibacterium prausnitzii 766.717491
## 3: Ruminococcaceae Faecalibacterium prausnitzii 939.220027
## 4: Ruminococcaceae Faecalibacterium prausnitzii 8.549888
## 5: Ruminococcaceae Faecalibacterium prausnitzii 119.847602
## ---
## 158: Peptostreptococcaceae Intestinibacter bartlettii 37.167487
## 159: Peptostreptococcaceae Terrisporobacter <NA> 2.827404
## 160: Rikenellaceae Alistipes <NA> 4.356718
## 161: Rikenellaceae Alistipes putredinis 36.369725
## 162: Verrucomicrobiaceae Akkermansia muciniphila 14.896185
## log2FoldChange lfcSE stat pvalue padj
## 1: -3.3601695 1.848755 -1.8175308 6.913588e-02 1.754690e-01
## 2: -2.6520420 1.700302 -1.5597474 1.188196e-01 2.499841e-01
## 3: -3.2863627 1.978889 -1.6607108 9.677154e-02 2.118512e-01
## 4: 0.5435519 2.896667 0.1876473 8.511531e-01 9.254148e-01
## 5: -10.4409639 2.051790 -5.0887104 3.605066e-07 3.893471e-06
## ---
## 158: -0.6025818 1.098780 -0.5484098 5.834106e-01 7.326551e-01
## 159: -1.9673140 2.813765 -0.6991750 4.844427e-01 6.715397e-01
## 160: -2.6660195 2.625130 -1.0155764 3.098312e-01 4.920848e-01
## 161: -8.7213663 2.032772 -4.2903802 1.783675e-05 1.699737e-04
## 162: -5.5548758 2.259363 -2.4586032 1.394787e-02 6.679572e-02
## Significant
## 1: FALSE
## 2: FALSE
## 3: FALSE
## 4: FALSE
## 5: TRUE
## ---
## 158: FALSE
## 159: FALSE
## 160: FALSE
## 161: TRUE
## 162: FALSEresdt.dds.NoAbx.Broad.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.0000000 NA
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.1383900 -1.388132
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.3537685 -2.308552
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: 2.986302 -0.4648330 0.6420510 NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: NA NA NA NA NA
## 1588: 2.973104 -0.7764788 0.4374663 NA NAvolcano.NoAbx.Broad.d7 = ggplot(
data = resdt.dds.NoAbx.Broad.d7[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.NoAbx.Broad.d7[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 No Antibiotics vs Broad Spectrum Antibiotics") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.NoAbx.Broad.d7
summary(res.dds.NoAbx.Broad.d7)##
## out of 480 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 8, 1.7%
## LFC < 0 (down) : 36, 7.5%
## outliers [1] : 0, 0%
## low counts [2] : 615, 128%
## (mean count < 3)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.NoAbx.Broad.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs No.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.NoAbx.Broad.d7)# Differential Abundance Testing
sample_data(ps1.NvB_9)$randomization_arm## [1] Narrow spectrum antibiotics Broad spectrum antibiotics
## [3] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [7] Broad spectrum antibiotics Broad spectrum antibiotics
## [9] Broad spectrum antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics
## [13] Broad spectrum antibiotics Narrow spectrum antibiotics
## [15] Broad spectrum antibiotics
## Levels: Narrow spectrum antibiotics Broad spectrum antibioticssample_data(ps1.NvB_9)$day## [1] -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9 -9ds.Narrow.Broad.d_9 <- phyloseq_to_deseq2(ps1.NvB_9, ~randomization_arm)
geoMeans.ds.Narrow.Broad.d_9 <- apply(counts(ds.Narrow.Broad.d_9), 1, gm_mean)
ds.Narrow.Broad.d_9 <- estimateSizeFactors(ds.Narrow.Broad.d_9, geoMeans = geoMeans.ds.Narrow.Broad.d_9)
dds.Narrow.Broad.d_9 <- DESeq(ds.Narrow.Broad.d_9, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.Narrow.Broad.d_9 = results(dds.Narrow.Broad.d_9, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d_9 = res.dds.Narrow.Broad.d_9[which(res.dds.Narrow.Broad.d_9$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d_9 = cbind(as(sigtab_dds.dds.Narrow.Broad.d_9, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d_9), ], "matrix"))
summary(res.dds.Narrow.Broad.d_9)##
## out of 433 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 7, 1.6%
## LFC < 0 (down) : 4, 0.92%
## outliers [1] : 0, 0%
## low counts [2] : 244, 56%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.Narrow.Broad.d_9)## baseMean
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 9.286714
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 28.824996
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 14.869014
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 61.275991
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 8.975456
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 31.766339
## log2FoldChange
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG -21.23749
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -23.63082
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT -22.70510
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 24.20964
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 21.53610
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT -23.76015
## lfcSE
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 2.995429
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.993679
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.994462
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 2.980967
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.984233
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.993603
## stat
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG -7.089968
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT -7.893571
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT -7.582366
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 8.121405
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 7.216631
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT -7.936973
## pvalue
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 1.341430e-12
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 2.936607e-15
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 3.393089e-14
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 4.608170e-16
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.329151e-13
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 2.071748e-15
## padj
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG 5.808391e-11
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT 4.238503e-13
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT 2.938415e-12
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG 1.995337e-13
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.563914e-11
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT 4.238503e-13
## Kingdom
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Bacteria
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteria
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteria
## Phylum
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Firmicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidetes
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Firmicutes
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Firmicutes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidetes
## Class
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Negativicutes
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidia
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Bacilli
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Clostridia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidia
## Order
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Selenomonadales
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Bacteroidales
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillales
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Clostridiales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidales
## Family
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Veillonellaceae
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotellaceae
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillaceae
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Lachnospiraceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroidaceae
## Genus
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG Dialister
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT Prevotella
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT Lactobacillus
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG Coprococcus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT Bacteroides
## Species
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG <NA>
## CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT <NA>
## GCGAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGAAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT ruminis
## GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG <NA>
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT <NA>
## GCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGTCGTTAAGTCAGCTGTGAAAGTTTGGGGCTCAACCTTAAAATTGCAGTTGATACTGGCGTCCTTGAGTGCGGTTGAGGTGTGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCACACTAAGCCGTAACTGACGTTCATGCTCGAAAGTGTGGGT <NA>write.table(sigtab_dds.dds.Narrow.Broad.d_9, file="./results/deseq_d_9_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d_9 = data.table(as(results(dds.Narrow.Broad.d_9, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d_9, "rn", "OTU")
taxdt.dds.d_9.com = data.table(data.frame(as(tax_table(ps1.NvB_9), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d_9.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d_9.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d_9, "OTU")
resdt.dds.Narrow.Broad.d_9 <- taxdt.dds.d_9.com[resdt.dds.Narrow.Broad.d_9]
resdt.dds.Narrow.Broad.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 0
## 1586: Bacteroidaceae Bacteroides <NA> 0 0
## 1587: Prevotellaceae Prevotella <NA> 0 NA
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 1584: NA NA NA NA
## 1585: 0 0 1 NA
## 1586: 0 0 1 NA
## 1587: NA NA NA NA
## 1588: NA NA NA NAresdt.dds.Narrow.Broad.d_9[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d_9[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGCTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTTTTCTTGAGTGCTGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTTCTGGACAGTAACTGACGTTGAGGCACGAAAGCGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTTAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 429: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 430: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATACCGGTGCTTAACACCGGAACTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 431: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 432: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 433: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 429: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 430: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 431: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 432: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 433: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium <NA> 8.9754565
## 2: Ruminococcaceae Faecalibacterium <NA> 0.2464832
## 3: Ruminococcaceae Faecalibacterium prausnitzii 1960.0861755
## 4: Ruminococcaceae Faecalibacterium <NA> 8.6146004
## 5: Ruminococcaceae Faecalibacterium prausnitzii 1679.8805890
## ---
## 429: Rikenellaceae Alistipes <NA> 0.2760191
## 430: Rikenellaceae Alistipes <NA> 4.4005966
## 431: Rikenellaceae Alistipes <NA> 48.4453684
## 432: Rikenellaceae Alistipes putredinis 218.3859563
## 433: Verrucomicrobiaceae Akkermansia muciniphila 19.4005140
## log2FoldChange lfcSE stat pvalue padj
## 1: 21.53610404 2.9842326 7.21663053 5.329151e-13 2.563914e-11
## 2: -1.45424762 3.0642601 -0.47458361 6.350838e-01 9.376016e-01
## 3: -0.25298945 0.9812585 -0.25782142 7.965447e-01 9.376016e-01
## 4: -6.39842244 2.9956388 -2.13591254 3.268654e-02 7.911656e-01
## 5: 0.01459924 0.8581001 0.01701345 9.864259e-01 9.932996e-01
## ---
## 429: 2.06951051 3.0405947 0.68062689 4.961076e-01 9.376016e-01
## 430: 5.68626669 2.9884024 1.90277813 5.706950e-02 9.376016e-01
## 431: 0.50772192 1.8421687 0.27561098 7.828469e-01 9.376016e-01
## 432: 1.21087498 1.2038839 1.00580710 3.145084e-01 9.376016e-01
## 433: -1.54544451 2.9013374 -0.53266625 5.942646e-01 9.376016e-01
## Significant
## 1: TRUE
## 2: FALSE
## 3: FALSE
## 4: FALSE
## 5: FALSE
## ---
## 429: FALSE
## 430: FALSE
## 431: FALSE
## 432: FALSE
## 433: FALSEresdt.dds.Narrow.Broad.d_9## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 0
## 1586: Bacteroidaceae Bacteroides <NA> 0 0
## 1587: Prevotellaceae Prevotella <NA> 0 NA
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: 0 0 1 NA NA
## 1586: 0 0 1 NA NA
## 1587: NA NA NA NA NA
## 1588: NA NA NA NA NAvolcano.Narrow.Broad.d_9 = ggplot(
data = resdt.dds.Narrow.Broad.d_9[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.Narrow.Broad.d_9[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay -9 Narrow Spectrum vs Broad Spectrum") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d_9
summary(res.dds.Narrow.Broad.d_9)##
## out of 433 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 7, 1.6%
## LFC < 0 (down) : 4, 0.92%
## outliers [1] : 0, 0%
## low counts [2] : 244, 56%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.Narrow.Broad.d_9, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.Narrow.Broad.d_9)# Differential Abundance Testing
sample_data(ps1.NvB.0)$randomization_arm## [1] Narrow spectrum antibiotics Broad spectrum antibiotics
## [3] Broad spectrum antibiotics Broad spectrum antibiotics
## [5] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [7] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [9] Broad spectrum antibiotics Narrow spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics
## [13] Narrow spectrum antibiotics Broad spectrum antibiotics
## [15] Broad spectrum antibiotics Narrow spectrum antibiotics
## [17] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [19] Broad spectrum antibiotics Broad spectrum antibiotics
## [21] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [23] Broad spectrum antibiotics
## Levels: Narrow spectrum antibiotics Broad spectrum antibioticssample_data(ps1.NvB.0)$day## [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0ds.Narrow.Broad.d0 <- phyloseq_to_deseq2(ps1.NvB.0, ~randomization_arm)
geoMeans.ds.Narrow.Broad.d0 <- apply(counts(ds.Narrow.Broad.d0), 1, gm_mean)
ds.Narrow.Broad.d0 <- estimateSizeFactors(ds.Narrow.Broad.d0, geoMeans = geoMeans.ds.Narrow.Broad.d0)
dds.Narrow.Broad.d0 <- DESeq(ds.Narrow.Broad.d0, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.Narrow.Broad.d0 = results(dds.Narrow.Broad.d0, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d0 = res.dds.Narrow.Broad.d0[which(res.dds.Narrow.Broad.d0$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d0 = cbind(as(sigtab_dds.dds.Narrow.Broad.d0, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d0), ], "matrix"))
summary(res.dds.Narrow.Broad.d0)##
## out of 170 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4, 2.4%
## LFC < 0 (down) : 15, 8.8%
## outliers [1] : 0, 0%
## low counts [2] : 205, 121%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.Narrow.Broad.d0)## baseMean
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 10.1113
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 604.4930
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1956.7376
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 349.3098
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 268.8318
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 155.9442
## log2FoldChange
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 7.630586
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG -3.972833
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG -8.544751
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT -26.533491
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT -8.559492
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG -25.425020
## lfcSE
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.591135
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.261105
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.418316
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 2.391792
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 1.565761
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 2.504133
## stat
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.944881
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG -3.150278
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG -6.024575
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT -11.093563
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT -5.466667
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG -10.153222
## pvalue
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 3.230785e-03
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.631149e-03
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.695551e-09
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 1.348095e-28
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 4.585763e-08
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 3.206019e-24
## padj
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.087584e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 1.141804e-02
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG 2.034661e-08
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT 1.132400e-26
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT 4.815051e-07
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG 1.346528e-22
## Kingdom
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Bacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Bacteria
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Bacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Bacteria
## Phylum
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Proteobacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Firmicutes
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Proteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Proteobacteria
## Class
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Gammaproteobacteria
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Negativicutes
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Deltaproteobacteria
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Betaproteobacteria
## Order
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriales
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Selenomonadales
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Desulfovibrionales
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Burkholderiales
## Family
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Enterobacteriaceae
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Acidaminococcaceae
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Desulfovibrionaceae
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Sutterellaceae
## Genus
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG Escherichia/Shigella
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT Phascolarctobacterium
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT Bilophila
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG Sutterella
## Species
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG <NA>
## GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT faecium
## GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT wadsworthia
## GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG stercoricaniswrite.table(sigtab_dds.dds.Narrow.Broad.d0, file="./results/deseq_d0_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d0 = data.table(as(results(dds.Narrow.Broad.d0, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d0, "rn", "OTU")
taxdt.dds.d0.com = data.table(data.frame(as(tax_table(ps1.NvB.0), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d0.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d0.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d0, "OTU")
resdt.dds.Narrow.Broad.d0 <- taxdt.dds.d0.com[resdt.dds.Narrow.Broad.d0]
resdt.dds.Narrow.Broad.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.1663214 -0.06235141
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.3416447 -0.83260200
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: NA NA NA NA
## 3: 3.021907 -0.02063313 0.9835383 NA
## 4: NA NA NA NA
## 5: NA NA NA NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: NA NA NA NA
## 1588: 3.011107 -0.27651024 0.7821562 NAresdt.dds.Narrow.Broad.d0[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d0[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: CCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGACGGTTGATTAAGTCTGATGTGAAAGCCCGGAGCTCAACTCCGGAAAGGCATTGGAAACTGGTCAACTTGAGTGCAGTAGAGGTAAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGCGGCGAAGGCGGCTTACTGGACTGTAACTGACGTTGAGGCTCGAAAGTGTGGGT
## 4: CCGAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTAGATAAGTCTGAAGTTAAAGGCTGTGGCTTAACCATAGTACGCTTTGGAAACTGTTTAACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 5: CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATATCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT
## 6: CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAATTGATACTGGCAGTCTTGAGTACAGTTGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTAACCTGTAACTGACATTGATGCTCGAAAGTGTGGGT
## 7: CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGTGGACAGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCTGTCTTGAGTACAGTAGAGGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGACTGCAACTGACACTGATGCTCGAAAGTGTGGGT
## 8: CCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTGATAAGTCTGAAGTTAAAGGCTGTGGCTCAACCATAGTTCGCTTTGGAAACTGTCAAACTTGAGTGCAGAAGGGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 9: CCGAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGTGGTTTATTAAGTCTGGTGTAAAAGGCAGTGGCTCAACCATTGTATGCATTGGAAACTGGTAGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCGGTGGCGAAAGCGGCTCTCTGGCCTGTAACTGACACTGAGGCTCGAAAGCGTGGGG
## 10: CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGACGCTCAACGTCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 11: CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGAACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 12: CCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCTGGAGATTAAGTGTGTTGTGAAATGTAGACGCTCAACGTCTGACTTGCAGCGCATACTGGTTTCCTTGAGTACGCACAACGTTGGCGGAATTCGTCGTGTAGCGGTGAAATGCTTAGATATGACGAAGAACTCCGATTGCGAAGGCAGCTGACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## 13: GCAAGCGTTAATCGGAATCACTGGGCGTAAAGCGCACGTAGGCGGCTTGGTAAGTCAGGGGTGAAATCCCACAGCCCAACTGTGGAACTGCCTTTGATACTGCCAGGCTTGAGTACCGGAGAGGGTGGCGGAATTCCAGGTGTAGGAGTGAAATCCGTAGATATCTGGAGGAACACCGGTGGCGAAGGCGGCCACCTGGACGGTAACTGACGCTGAGGTGCGAAAGCGTGGGT
## 14: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 15: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 16: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTCAAGTCGGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTCGAAACTGGCAGGCTGGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 17: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTCTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGAAACTGGCAGGCTTGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 18: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTGATTAAGTCAGATGTGAAATCCCCGAGCTTAACTTGGGAACTGCATTTGAAACTGGTCAGCTAGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 19: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATCTGATACTGGCAAGCTTGAGTCTCGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACGAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 20: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGTTTGTTAAGTCAGATGTGAAATCCCCGGGCTCAACCTGGGAACTGCATTTGAAACTGGCAAGCTTGAGTCTTGTAGAGGGGGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGGTGGCGAAGGCGGCCCCCTGGACAAAGACTGACGCTCAGGTGCGAAAGCGTGGGG
## 21: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCGCTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAACTGCATTTGTGACTGGCGGGCTAGAGTATGGCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGAGATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGCCAATACTGACGCTCATGCACGAAAGCGTGGGG
## 22: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG
## 23: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAATTGCATTTGTGACTGCAGGACTAGAGTTCATCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCAATGGCGAAGGCAGCCCCCTGGGATGCGACTGACGCTCATGCACGAAAGCGTGGGG
## 24: GCAAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTCTGTAAGATAGATGTGAAATCCCCGGGCTCAACCTGGGAATTGCATATATGACTGCAGGACTTGAGTTTGTCAGAGGAGGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAAGAACACCGATGGCGAAGGCAGCCCTCTGGGACATGACTGACGCTCATGCACGAAAGCGTGGGG
## 25: GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG
## 26: GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 27: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGGACTGCAAGTTGGATGTGAAATACCGTGGCTTAACCACGGAACTGCATCCAAAACTGTAGTTCTTGAGTGAAGTAGAGGCAAGCGGAATTCCGAGTGTAGCGGTGAAATGCGTAGATATTCGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGGCTTTAACTGACGCTGAGGCTCGAAAGTGTGGGG
## 28: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGCAGGCGGTGCGGCAAGTCTGATGTGAAAGCCCGGGGCTCAACCCCGGTACTGCATTGGAAACTGTCGTACTAGAGTGTCGGAGGGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 29: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCAAGGCAAGTCTGAAGTGAAAGCCCGGTGCTTAACGCCGGGACTGCTTTGGAAACTGTTTGGCTGGAGTGCCGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTACTGGACGGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 30: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCGAAGCAAGTCTGAAGTGAAAGCCCGGGGCTTAACCCCGGGACTGCTTTGGAAACTGTTTTGCTAGAGTGCTGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 31: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGCTGTGCAAGTCTGAAGTGAAAGGCATGGGCTCAACCTGTGGACTGCTTTGGAAACTGTGCAGCTAGAGTGTCGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACGATGACTGACGTTGAGGCTCGAAAGCGTGGGG
## 32: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGAGCGTAGACGGTAAAGCAAGTCTGAAGTGAAAGCCCGGGGCTCAACCCCGGGACTGCTTTGGAAACTGTTTAACTAGAGTGCTGGAGAGGTAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTACTGGACAGTAACTGACGTTGAGGCTCGAAAGCGTGGGG
## 33: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGTGTAGGCGGGATTGCAAGTCAGATGTGAAAACTGGGGGCTCAACCTCCAGCCTGCATTTGAAACTGTAGTTCTTGAGTGCTGGAGAGGCAATCGGAATTCCGTGTGTAGCGGTGAAATGCGTAGATATACGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACAGTAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 34: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGCAAGGCAAGTCTGAAGTGAAAATCCGGGGCTCAACCCCGGAACTGCTTTGGAAACTGTTTAGCTGGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG
## 35: GCAAGCGTTATCCGGATTTACTGGGTGTAAAGGGTGCGTAGGTGGTGAGACAAGTCTGAAGTGAAAATCCGGGGCTTAACCCCGGAACTGCTTTGGAAACTGCCTGACTAGAGTACAGGAGAGGTAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGACTTACTGGACTGCTACTGACACTGAGGCACGAAAGCGTGGGG
## 36: GCAAGCGTTATCCGGATTTATTGGGCGTAAAGAGAGTGCAGGCGGTTTTCTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGAAGTGCATCGGAAACTGGATAACTTGAGTGCAGAAGAGGGTAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTACCTGGTCTGCAACTGACGCTGAGACTCGAAAGCATGGGT
## 37: GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTCTTTTAAGTCTAATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 38: GCAAGCGTTATCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT
## 39: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGGCTTTTAAGTCTGACGTGAAAATGCGGGGCTTAACCCCGTATGGCGTTGGATACTGGAAGTCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT
## 40: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATCAGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCTGATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 41: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATCAGTTAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCTGATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 42: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATTGGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTACCAATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 43: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGATTGGTCAGTCTGTCTTAAAAGTTCGGGGCTTAACCCCGTGATGGGATGGAAACTGCCAATCTAGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 44: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCCGTGCAAGTCCATCTTAAAAGCGTGGGGCTTAACCCCATGAGGGGATGGAAACTGCAGGGCTGGAGTGTCGGAGGGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTAGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 45: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGCTTCTTAAGTCCATCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGAGAGGCTGGAGTATCGGAGAGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCGGTGGCGAAGGCGACTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 46: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGCGGTTTCATAAGTCTGTCTTAAAAGTGCGGGGCTTAACCCCGTGAGGGGATGGAAACTATGGAACTGGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 47: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGCGCGCGCAGGTGGTTTAATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAGGGTCATTGGAAACTGTTAAACTTGAGTGCAGGAGAGAAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTTTGGCCTGTAACTGACACTGAGGCGCGAAAGCGTGGGG
## 48: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT
## 49: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCGCGCAGGCGGCGTCGTAAGTCGGTCTTAAAAGTGCGGGGCTTAACCCCGTGAGGGGACCGAAACTGCGATGCTAGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAAGCGGCTTTCTGGACGACAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 50: GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCGCGCAGGCGGCTTCTTAAGTCTGTCTTAAAAGTGCGGGGCTTAACCCCGTGATGGGATGGAAACTGGGAAGCTCAGAGTATCGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAAGCGGCTTTCTGGACGAAAACTGACGCTGAGGCGCGAAAGCCAGGGG
## 51: GCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGAGATGTGAAATACCCGGGCTTAACTTGGGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGAGAGGAGAAGGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGCTTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 52: GCAAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGGGATGTGAAATACCCGGGCTCAACCTGGGTGCTGCATTCCAAACTGGAAATCTAGAGTGCAGGAGGGGAAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 53: GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGCGTGCAGCCGGGTCTGCAAGTCAGATGTGAAATCCATGGGCTCAACCCATGAACTGCATTTGAAACTGTAGATCTTGAGTGTCGGAGGGGCAATCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGATTGCTGGACGATAACTGACGGTGAGGCGCGAAAGTGTGGGG
## 54: GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT
## 55: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAAGAATAAGTCTGATGTGAAAGCCCTCGGCTTAACCGAGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 56: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAATGATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAACTGCATCGGAAACTGTCATTCTTGAGTGCAGAAGAGGAGAGTGGAATTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 57: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGAGAAATAAGTCTGATGTGAAAGCCCACGGCTCAACCGTGGAACTGCATCGGAAACTGTTTTTCTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGGAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGCAACTGACGCTGAGGCTCGAAAGCATGGGT
## 58: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCCCCGGCTCAACCGGGGAGGGTCATTGGAAACTGGGAGACTTGAGTGCAGAAGAGGAGAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGGCTCTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 59: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTCTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGCATGGGT
## 60: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGCGAGCGCAGGCGGTTTTTTAAGTCTGATGTGAAAGCCTTCGGCTCAACCGAAGAAGTGCATCGGAAACTGGGAAACTTGAGTGCAGAAGAGGACAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAGGCGGCTGTCTGGTCTGTAACTGACGCTGAGGCTCGAAAGTATGGGT
## 61: GCAAGCGTTGTCCGGATTTATTGGGCGTAAAGGGAACGCAGGCGGTCTTTTAAGTCTGATGTGAAAGCCTTCGGCTTAACCGGAGTAGTGCATTGGAAACTGGAAGACTTGAGTGCAGAAGAGGAGAGTGGAACTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAAGAACACCAGTGGCGAAAGCGGCTCTCTGGTCTGTAACTGACGCTGAGGTTCGAAAGCGTGGGT
## 62: GCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGGGTAAGACAGATGTGAAATCCCCGGGCTTAACCTGGGAACTGCATTTGTGACTGTCCGACTGGAGTATGTCAGAGGGGGGTGGAATTCCAAGTGTAGCAGTGAAATGCGTAGATATTTGGAAGAACACCGATGGCGAAGGCAGCCCCCTGGGGCAAAACTGACGCTCATGCACGAAAGCGTGGGG
## 63: GCGAGCGTTAATCGGAATTACTGGGCGTAAAGCGTGCGCAGGCGGTTGTGTAAGACAGATGTGAAATGCCCGGGCTTAACCTGGGAATTGCATTTGTGACTGCACGGCTAGAGTGTGTCAGAGGGGGGTAGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAGGAATACCGATGGCGAAGGCAGCCCCCTGGGATAACACTGACGCTCATGCACGAAAGCGTGGGG
## 64: GCGAGCGTTAATCGGAATTACTGGGCGTAAAGGGTGCGCAGGCGGTTGAGTAAGACAGATGTGAAATCCCCGAGCTTAACTCGGGAATGGCATATGTGACTGCTCGACTAGAGTGTGTCAGAGGGAGGTGGAATTCCACGTGTAGCAGTGAAATGCGTAGATATGTGGAAGAACACCGATGGCGAAGGCAGCCTCCTGGGACATAACTGACGCTCAGGCACGAAAGCGTGGGG
## 65: GCGAGCGTTAATCGGATTTACTGGGCGTAAAGCGTGCGTAGGCGGCTTTTTAAGTCGGATGTGAAATCCCTGAGCTTAACTTAGGAATTGCATTCGATACTGGGAAGCTAGAGTATGGGAGAGGATGGTAGAATTCCAGGTGTAGCGGTGAAATGCGTAGAGATCTGGAGGAATACCGATGGCGAAGGCAGCCATCTGGCCTAATACTGACGCTGAGGTACGAAAGCATGGGG
## 66: GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGCGCGCAGGTGGTTGATTAAGTCTGATGTGAAAGCCCACGGCTTAACCGTGGAGGGTCATTGGAAACTGGTCGACTTGAGTGCAGAAGAGGGAAGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGAGATATGGAGGAACACCAGTGGCGAAGGCGGCTTCCTGGTCTGTAACTGACACTGAGGCGCGAAAGCGTGGGG
## 67: GCGAGCGTTATCCGGAATTATTGGGCGTAAAGAGGGAGCAGGCGGCACTAAGGGTCTGTGGTGAAAGATCGAAGCTTAACTTCGGTAAGCCATGGAAACCGTAGAGCTAGAGTGTGTGAGAGGATCGTGGAATTCCATGTGTAGCGGTGAAATGCGTAGATATATGGAGGAACACCAGTGGCGAAGGCGACGATCTGGCGCATAACTGACGCTCAGTCCCGAAAGCGTGGGG
## 68: GCGAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGTAGGCGGGAATGCAAGTCAGATGTGAAATCCAGGGGCTTAACCCTTGAACTGCATTTGAAACTGTATTTCTTGAGTGTCGGAGAGGTTGACGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGTCAACTGGACGATAACTGACGCTGAGGCGCGAAAGCGTGGGG
## 69: GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGCAGACGGGGGATTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGTTCCCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCATAGATATCACGAAGAACCCCGATTGCGAAGGCAGCCTGCTAAGCTGTAACTGACGTTGAGGCTCGAAAGTGTGGGT
## 70: GCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGTGGTGATTTAAGTCAGCGGTGAAAGTTTGTGGCTCAACCATAAAATTGCCGTTGAAACTGGGTTACTTGAGTGTGTTTGAGGTAGGCGGAATGCGTGGTGTAGCGGTGAAATGCATAGATATCACGCAGAACTCCGATTGCGAAGGCAGCTTACTAAACCATAACTGACACTGAAGCACGAAAGCGTGGGG
## 71: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCATGTAGGCGGCTTAATAAGTCGAGCGTGAAAATGCGGGGCTCAACCCCGTATGGCGCTGGAAACTGTTAGGCTTGAGTGCAGGAGAGGAAAGGGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAGGAACACCAGTGGCGAAGGCGCCTTTCTGGACTGTGTCTGACGCTGAGATGCGAAAGCCAGGGT
## 72: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGGCCGGAGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGAAGCGAAAGCATGGGG
## 73: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGAGCTTGTAGGCGGTTTGTCGCGTCTGCTGTGAAAGGCCGGGGCTTAACTCCGTGTATTGCAGTGGGTACGGGCAGACTAGAGTGCAGTAGGGGAGACTGGAATTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAGGAACACCGATGGCGAAGGCAGGTCTCTGGGCTGTAACTGACGCTGAGAAGCGAAAGCATGGGG
## 74: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAGGTAAGTCTATCTTAAAAGTGCGGGGCTCAACCCCGTGAGGGGATGGAAACTATCTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 75: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAGGTCAAGTCTATCTTAAAAGTGCGGGGCTCAACCCCGTGAGGGGATGGAAACTGGTCTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 76: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTGGTCGCGTCTGCCGTGAAATCCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCTGACTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 77: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTGGTCGCGTCTGCCGTGAAATTCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCTGACTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 78: GCGAGCGTTGTCCGGAATTATTGGGCGTAAAGGGCTTGTAGGCGGTTTGTCGCGTCTGCCGTGAAATCCTCTGGCTTAACTGGGGGCGTGCGGTGGGTACGGGCAGGCTTGAGTGCGGTAGGGGAGACTGGAACTCCTGGTGTAGCGGTGGAATGCGCAGATATCAGGAAGAACACCGGTGGCGAAGGCGGGTCTCTGGGCCGTTACTGACGCTGAGGAGCGAAAGCGTGGGG
## 79: GCGAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGACTTTTAAGTGAGATGTGAAATACCCGGGCTCAACTTGGGTGCTGCATTTCAAACTGGAAGTCTAGAGTGCAGGAGAGGAGAATGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGATTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 80: GCGAGCGTTGTCCGGATTTACTGGGCGTAAAGGGAGCGTAGGCGGATTTTTAAGTGGGATGTGAAATACCCGGGCTCAACCTGGGTGCTGCATTCCAAACTGGGAATCTAGAGTGCAGGAGGGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGAGATTAGGAAGAACACCAGTGGCGAAGGCGACTCTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCGTGGGG
## 81: GCGAGCGTTGTTCGGAATTACTGGGCGTAAAGCGCACGCAGGCGGACCAGTAAGTCTGTCGTCAAAGGCGGAGGCTCAACCTTCGTTCCACGATAGATACTGCGGGTCTAGAGTATGTGAGAGGGAAGTGGAATTCCCGGTGTAGCGGTGAAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCTTCCTGGCACACAACTGACGCTCATGTGCGAAAGCCAGGGC
## 82: GCTAGCGTTATCCGGAATTACTGGGCGTAAAGGGTGCGTAGGTGGTTTCTTAAGTCAGAGGTGAAAGGCTACGGCTCAACCGTAGTAAGCCTTTGAAACTGGGAAACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACACTGAGGCACGAAAGCGTGGGG
## 83: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 84: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## OTU
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Bacilli Lactobacillales
## 4: Bacteria Firmicutes Bacilli Lactobacillales
## 5: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 6: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 7: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 8: Bacteria Firmicutes Bacilli Lactobacillales
## 9: Bacteria Firmicutes Bacilli Lactobacillales
## 10: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 11: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 12: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 13: Bacteria Proteobacteria Deltaproteobacteria Desulfovibrionales
## 14: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 15: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 16: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 17: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 18: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 19: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 20: Bacteria Proteobacteria Gammaproteobacteria Enterobacteriales
## 21: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 22: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 23: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 24: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 25: Bacteria Actinobacteria Actinobacteria Bifidobacteriales
## 26: Bacteria Actinobacteria Actinobacteria Bifidobacteriales
## 27: Bacteria Firmicutes Clostridia Clostridiales
## 28: Bacteria Firmicutes Clostridia Clostridiales
## 29: Bacteria Firmicutes Clostridia Clostridiales
## 30: Bacteria Firmicutes Clostridia Clostridiales
## 31: Bacteria Firmicutes Clostridia Clostridiales
## 32: Bacteria Firmicutes Clostridia Clostridiales
## 33: Bacteria Firmicutes Clostridia Clostridiales
## 34: Bacteria Firmicutes Clostridia Clostridiales
## 35: Bacteria Firmicutes Clostridia Clostridiales
## 36: Bacteria Firmicutes Bacilli Lactobacillales
## 37: Bacteria Firmicutes Bacilli Lactobacillales
## 38: Bacteria Firmicutes Bacilli Lactobacillales
## 39: Bacteria Firmicutes Negativicutes Selenomonadales
## 40: Bacteria Firmicutes Negativicutes Selenomonadales
## 41: Bacteria Firmicutes Negativicutes Selenomonadales
## 42: Bacteria Firmicutes Negativicutes Selenomonadales
## 43: Bacteria Firmicutes Negativicutes Selenomonadales
## 44: Bacteria Firmicutes Negativicutes Selenomonadales
## 45: Bacteria Firmicutes Negativicutes Selenomonadales
## 46: Bacteria Firmicutes Negativicutes Selenomonadales
## 47: Bacteria Firmicutes Bacilli Bacillales
## 48: Bacteria Firmicutes Negativicutes Selenomonadales
## 49: Bacteria Firmicutes Negativicutes Selenomonadales
## 50: Bacteria Firmicutes Negativicutes Selenomonadales
## 51: Bacteria Firmicutes Clostridia Clostridiales
## 52: Bacteria Firmicutes Clostridia Clostridiales
## 53: Bacteria Firmicutes Clostridia Clostridiales
## 54: Bacteria Firmicutes Clostridia Clostridiales
## 55: Bacteria Firmicutes Bacilli Lactobacillales
## 56: Bacteria Firmicutes Bacilli Lactobacillales
## 57: Bacteria Firmicutes Bacilli Lactobacillales
## 58: Bacteria Firmicutes Bacilli Lactobacillales
## 59: Bacteria Firmicutes Bacilli Lactobacillales
## 60: Bacteria Firmicutes Bacilli Lactobacillales
## 61: Bacteria Firmicutes Bacilli Lactobacillales
## 62: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 63: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 64: Bacteria Proteobacteria Betaproteobacteria Burkholderiales
## 65: Bacteria Proteobacteria Gammaproteobacteria Pseudomonadales
## 66: Bacteria Firmicutes Erysipelotrichia Erysipelotrichales
## 67: Bacteria Firmicutes Erysipelotrichia Erysipelotrichales
## 68: Bacteria Firmicutes Clostridia Clostridiales
## 69: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 70: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 71: Bacteria Firmicutes Negativicutes Selenomonadales
## 72: Bacteria Actinobacteria Actinobacteria Actinomycetales
## 73: Bacteria Actinobacteria Actinobacteria Actinomycetales
## 74: Bacteria Firmicutes Negativicutes Selenomonadales
## 75: Bacteria Firmicutes Negativicutes Selenomonadales
## 76: Bacteria Actinobacteria Actinobacteria Actinomycetales
## 77: Bacteria Actinobacteria Actinobacteria Actinomycetales
## 78: Bacteria Actinobacteria Actinobacteria Actinomycetales
## 79: Bacteria Firmicutes Clostridia Clostridiales
## 80: Bacteria Firmicutes Clostridia Clostridiales
## 81: Bacteria Synergistetes Synergistia Synergistales
## 82: Bacteria Firmicutes Clostridia Clostridiales
## 83: Bacteria Firmicutes Clostridia Clostridiales
## 84: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Kingdom Phylum Class Order
## Family Genus
## 1: Ruminococcaceae Faecalibacterium
## 2: Ruminococcaceae Faecalibacterium
## 3: Leuconostocaceae Leuconostoc
## 4: Streptococcaceae Streptococcus
## 5: Bacteroidaceae Bacteroides
## 6: Bacteroidaceae Bacteroides
## 7: Bacteroidaceae Bacteroides
## 8: Streptococcaceae Streptococcus
## 9: Streptococcaceae Lactococcus
## 10: Prevotellaceae Prevotella
## 11: Prevotellaceae Prevotella
## 12: Prevotellaceae Prevotella
## 13: Desulfovibrionaceae Bilophila
## 14: Enterobacteriaceae Citrobacter
## 15: Enterobacteriaceae Enterobacter
## 16: Enterobacteriaceae <NA>
## 17: Enterobacteriaceae <NA>
## 18: Enterobacteriaceae Hafnia
## 19: Enterobacteriaceae Escherichia/Shigella
## 20: Enterobacteriaceae <NA>
## 21: Burkholderiaceae Burkholderia
## 22: Sutterellaceae Sutterella
## 23: Sutterellaceae Sutterella
## 24: Sutterellaceae Sutterella
## 25: Bifidobacteriaceae Bifidobacterium
## 26: Bifidobacteriaceae Bifidobacterium
## 27: Ruminococcaceae Clostridium_IV
## 28: Lachnospiraceae Roseburia
## 29: Lachnospiraceae Fusicatenibacter
## 30: Lachnospiraceae Clostridium_XlVa
## 31: Lachnospiraceae Dorea
## 32: Lachnospiraceae Clostridium_XlVa
## 33: Ruminococcaceae Flavonifractor
## 34: Lachnospiraceae Coprococcus
## 35: Lachnospiraceae Coprococcus
## 36: Lactobacillaceae Lactobacillus
## 37: Lactobacillaceae Pediococcus
## 38: Lactobacillaceae Lactobacillus
## 39: Acidaminococcaceae Acidaminococcus
## 40: Veillonellaceae Veillonella
## 41: Veillonellaceae Veillonella
## 42: Veillonellaceae Veillonella
## 43: Veillonellaceae Veillonella
## 44: Veillonellaceae Allisonella
## 45: Veillonellaceae Dialister
## 46: Veillonellaceae Veillonella
## 47: Bacillales_Incertae_Sedis_XI Gemella
## 48: Veillonellaceae Megamonas
## 49: Veillonellaceae Megasphaera
## 50: Veillonellaceae Megasphaera
## 51: Clostridiaceae_1 Clostridium_sensu_stricto
## 52: Clostridiaceae_1 Clostridium_sensu_stricto
## 53: Ruminococcaceae <NA>
## 54: Ruminococcaceae Ruminococcus
## 55: Lactobacillaceae Lactobacillus
## 56: Lactobacillaceae Lactobacillus
## 57: Lactobacillaceae Lactobacillus
## 58: Enterococcaceae Enterococcus
## 59: Lactobacillaceae Lactobacillus
## 60: Lactobacillaceae Lactobacillus
## 61: Lactobacillaceae Lactobacillus
## 62: Sutterellaceae <NA>
## 63: Oxalobacteraceae <NA>
## 64: Sutterellaceae Parasutterella
## 65: Moraxellaceae Acinetobacter
## 66: Erysipelotrichaceae Turicibacter
## 67: Erysipelotrichaceae Clostridium_XVIII
## 68: Ruminococcaceae <NA>
## 69: Bacteroidaceae Bacteroides
## 70: Porphyromonadaceae Parabacteroides
## 71: Acidaminococcaceae Phascolarctobacterium
## 72: Micrococcaceae Rothia
## 73: Micrococcaceae Rothia
## 74: Veillonellaceae Mitsuokella
## 75: Veillonellaceae Mitsuokella
## 76: Actinomycetaceae Actinomyces
## 77: Actinomycetaceae Actinomyces
## 78: Actinomycetaceae Actinomyces
## 79: Clostridiaceae_1 Clostridium_sensu_stricto
## 80: Clostridiaceae_1 Clostridium_sensu_stricto
## 81: Synergistaceae Cloacibacillus
## 82: Peptostreptococcaceae Romboutsia
## 83: Peptostreptococcaceae Intestinibacter
## 84: Verrucomicrobiaceae Akkermansia
## Family Genus
## Species baseMean log2FoldChange lfcSE stat
## 1: prausnitzii 3.1390305 5.9442601 2.973056 1.99937704
## 2: prausnitzii 10.1113002 7.6305860 2.591135 2.94488136
## 3: <NA> 0.5670333 3.5189396 3.014336 1.16740129
## 4: <NA> 6.8415384 4.5388080 2.945712 1.54081893
## 5: <NA> 7.2662289 2.4404837 2.162371 1.12861475
## 6: massiliensis 1.2401237 4.5896686 2.988136 1.53596390
## 7: <NA> 2.0123037 2.2442982 2.955671 0.75931934
## 8: <NA> 171.2643089 1.0820588 1.224416 0.88373453
## 9: <NA> 6.8013088 2.3304887 2.367147 0.98451384
## 10: copri 76.9153769 -2.5117179 2.661498 -0.94372349
## 11: <NA> 2.5451127 1.2458367 2.941859 0.42348612
## 12: histicola 0.6319000 -1.6624347 2.998334 -0.55445274
## 13: wadsworthia 268.8317952 -8.5594919 1.565761 -5.46666703
## 14: <NA> 8.5239695 -21.2166263 2.982198 -7.11442686
## 15: <NA> 128.0705643 -5.5989889 2.455047 -2.28060392
## 16: <NA> 1956.7376403 -8.5447507 1.418316 -6.02457451
## 17: <NA> 1.2221755 -2.6534371 2.989453 -0.88759963
## 18: alvei 0.9006996 -2.2069847 2.992726 -0.73744968
## 19: <NA> 604.4929876 -3.9728330 1.261105 -3.15027844
## 20: <NA> 0.7467910 -1.9083905 2.995543 -0.63707664
## 21: <NA> 1.1220941 4.4458211 2.990718 1.48653953
## 22: stercoricanis 155.9441517 -25.4250205 2.504133 -10.15322158
## 23: <NA> 24.0180682 -22.8642206 2.813303 -8.12717947
## 24: wadsworthensis 67.7843959 -8.4408338 2.117932 -3.98541349
## 25: <NA> 0.6617053 3.6764425 3.009770 1.22150261
## 26: <NA> 4.4294496 2.9712738 2.091317 1.42076713
## 27: <NA> 0.6483125 3.4342201 3.016796 1.13836651
## 28: <NA> 5.4676945 4.0882424 2.774161 1.47368623
## 29: <NA> 0.5514211 3.4342201 3.016796 1.13836651
## 30: <NA> 0.8318677 4.0237852 2.999934 1.34129107
## 31: formicigenerans 0.7939249 3.9714865 3.001272 1.32326795
## 32: <NA> 0.7957836 1.3884862 2.999945 0.46283728
## 33: plautii 1.2404000 4.5997366 2.987964 1.53942156
## 34: <NA> 0.9155733 4.1591661 2.996685 1.38792215
## 35: <NA> 1.1669626 4.4937078 2.989830 1.50299759
## 36: <NA> 0.6617053 3.6764425 3.009770 1.22150261
## 37: <NA> 195.1423429 9.3574474 2.615391 3.57783930
## 38: <NA> 44.9791388 -7.8484046 2.435579 -3.22239756
## 39: <NA> 20.6753613 -3.5485640 2.935158 -1.20898570
## 40: <NA> 0.6405347 -1.6814551 2.998101 -0.56083997
## 41: <NA> 1.5866839 -3.0284352 2.987380 -1.01374285
## 42: <NA> 1.2865005 -2.7270986 2.989002 -0.91237754
## 43: <NA> 55.0316077 -6.2673007 2.597626 -2.41270311
## 44: histaminiformans 0.8891927 -2.2100932 2.992699 -0.73849491
## 45: <NA> 2.2136640 2.7777882 2.726675 1.01874558
## 46: <NA> 13.3273462 -21.9810648 2.981726 -7.37192755
## 47: <NA> 3.2785365 4.6696428 2.267994 2.05893107
## 48: funiformis 233.2355987 -7.6497327 2.945067 -2.59747362
## 49: <NA> 0.6237367 3.6428814 3.010734 1.20996463
## 50: micronuciformis 89.0482161 -4.7614791 2.307817 -2.06319593
## 51: cellulovorans 0.5670333 3.5189396 3.014336 1.16740129
## 52: paraputrificum 0.8108166 -2.0654478 2.993990 -0.68986467
## 53: <NA> 2.0296074 1.5432424 2.758483 0.55945320
## 54: <NA> 1.2432599 2.0627999 2.977146 0.69287843
## 55: <NA> 0.7869974 3.9594093 3.001587 1.31910522
## 56: delbrueckii 2.8139592 -0.2825662 2.945301 -0.09593796
## 57: kalixensis 0.7394379 3.8533599 3.004472 1.28254137
## 58: <NA> 0.7481220 1.6009486 3.002997 0.53311697
## 59: <NA> 6.6148627 22.6774259 2.969346 7.63717739
## 60: <NA> 177.3227612 2.2093872 1.713957 1.28905648
## 61: salivarius 1.6771647 -3.1035423 2.987025 -1.03900767
## 62: <NA> 1.0926032 4.4188393 2.991232 1.47726413
## 63: <NA> 1.9201883 -3.2930334 2.986208 -1.10274761
## 64: excrementihominis 29.8594555 -6.2815264 2.513662 -2.49895450
## 65: <NA> 0.7130229 -1.8843736 2.995795 -0.62900609
## 66: <NA> 1.6768222 -3.1074859 2.677216 -1.16071550
## 67: <NA> 0.6604966 1.4795366 3.007301 0.49198157
## 68: <NA> 0.5545784 3.4563143 3.016258 1.14589474
## 69: <NA> 1.2966251 4.6479013 2.987160 1.55595983
## 70: merdae 1.0385625 4.3441712 2.992702 1.45158809
## 71: faecium 349.3097927 -26.5334911 2.391792 -11.09356272
## 72: mucilaginosa 0.6732565 3.7000399 3.009027 1.22964679
## 73: mucilaginosa 2.0208216 5.3146081 2.978370 1.78440130
## 74: jalaludinii 2.0002740 -2.6407803 2.983886 -0.88501372
## 75: <NA> 1.5438006 -2.9890537 2.987574 -1.00049544
## 76: odontolyticus 14.5512433 6.2353391 2.147200 2.90394011
## 77: odontolyticus 2.4255187 -3.6338470 2.711006 -1.34040528
## 78: <NA> 3.5719250 6.1256910 2.971906 2.06119933
## 79: <NA> 0.5438958 -1.4808218 3.000716 -0.49348950
## 80: baratii 0.7400080 -1.9424271 2.995192 -0.64851502
## 81: evryensis 18.6608616 -5.8656361 2.978407 -1.96938721
## 82: <NA> 16.2747242 -3.7583810 2.030637 -1.85083866
## 83: bartlettii 1.0349767 0.4350350 2.986993 0.14564315
## 84: muciniphila 634.1688355 -6.8224822 2.679427 -2.54624704
## Species baseMean log2FoldChange lfcSE stat
## pvalue padj Significant
## 1: 4.556757e-02 1.664207e-01 FALSE
## 2: 3.230785e-03 2.087584e-02 TRUE
## 3: 2.430484e-01 3.956559e-01 FALSE
## 4: 1.233609e-01 3.472809e-01 FALSE
## 5: 2.590604e-01 3.956559e-01 FALSE
## 6: 1.245472e-01 3.472809e-01 FALSE
## 7: 4.476615e-01 5.610335e-01 FALSE
## 8: 3.768395e-01 4.796140e-01 FALSE
## 9: 3.248629e-01 4.473522e-01 FALSE
## 10: 3.453110e-01 4.678407e-01 FALSE
## 11: 6.719406e-01 6.883294e-01 FALSE
## 12: 5.792690e-01 6.319299e-01 FALSE
## 13: 4.585763e-08 4.815051e-07 TRUE
## 14: 1.123788e-12 1.573304e-11 TRUE
## 15: 2.257190e-02 9.979154e-02 FALSE
## 16: 1.695551e-09 2.034661e-08 TRUE
## 17: 3.747562e-01 4.796140e-01 FALSE
## 18: 4.608489e-01 5.610335e-01 FALSE
## 19: 1.631149e-03 1.141804e-02 TRUE
## 20: 5.240749e-01 6.008782e-01 FALSE
## 21: 1.371365e-01 3.472809e-01 FALSE
## 22: 3.206019e-24 1.346528e-22 TRUE
## 23: 4.393946e-16 1.230305e-14 TRUE
## 24: 6.736272e-05 6.287187e-04 TRUE
## 25: 2.218958e-01 3.956559e-01 FALSE
## 26: 1.553845e-01 3.625638e-01 FALSE
## 27: 2.549675e-01 3.956559e-01 FALSE
## 28: 1.405661e-01 3.472809e-01 FALSE
## 29: 2.549675e-01 3.956559e-01 FALSE
## 30: 1.798260e-01 3.833964e-01 FALSE
## 31: 1.857463e-01 3.833964e-01 FALSE
## 32: 6.434810e-01 6.673136e-01 FALSE
## 33: 1.237014e-01 3.472809e-01 FALSE
## 34: 1.651608e-01 3.749595e-01 FALSE
## 35: 1.328397e-01 3.472809e-01 FALSE
## 36: 2.218958e-01 3.956559e-01 FALSE
## 37: 3.464463e-04 2.910149e-03 TRUE
## 38: 1.271226e-03 9.707545e-03 TRUE
## 39: 2.266683e-01 3.956559e-01 FALSE
## 40: 5.749066e-01 6.319299e-01 FALSE
## 41: 3.107055e-01 4.423603e-01 FALSE
## 42: 3.615700e-01 4.796140e-01 FALSE
## 43: 1.583471e-02 7.389532e-02 FALSE
## 44: 4.602138e-01 5.610335e-01 FALSE
## 45: 3.083238e-01 4.423603e-01 FALSE
## 46: 1.681784e-13 2.825397e-12 TRUE
## 47: 3.950084e-02 1.508214e-01 FALSE
## 48: 9.391233e-03 5.259091e-02 FALSE
## 49: 2.262925e-01 3.956559e-01 FALSE
## 50: 3.909402e-02 1.508214e-01 FALSE
## 51: 2.430484e-01 3.956559e-01 FALSE
## 52: 4.902793e-01 5.800487e-01 FALSE
## 53: 5.758525e-01 6.319299e-01 FALSE
## 54: 4.883858e-01 5.800487e-01 FALSE
## 55: 1.871339e-01 3.833964e-01 FALSE
## 56: 9.235698e-01 9.235698e-01 FALSE
## 57: 1.996528e-01 3.900194e-01 FALSE
## 58: 5.939526e-01 6.396413e-01 FALSE
## 59: 2.220355e-14 4.662745e-13 TRUE
## 60: 1.973785e-01 3.900194e-01 FALSE
## 61: 2.988012e-01 4.403386e-01 FALSE
## 62: 1.396048e-01 3.472809e-01 FALSE
## 63: 2.701368e-01 4.052052e-01 FALSE
## 64: 1.245603e-02 6.154744e-02 FALSE
## 65: 5.293451e-01 6.008782e-01 FALSE
## 66: 2.457576e-01 3.956559e-01 FALSE
## 67: 6.227324e-01 6.538690e-01 FALSE
## 68: 2.518387e-01 3.956559e-01 FALSE
## 69: 1.197176e-01 3.472809e-01 FALSE
## 70: 1.466162e-01 3.518788e-01 FALSE
## 71: 1.348095e-28 1.132400e-26 TRUE
## 72: 2.188294e-01 3.956559e-01 FALSE
## 73: 7.435848e-02 2.402351e-01 FALSE
## 74: 3.761492e-01 4.796140e-01 FALSE
## 75: 3.170708e-01 4.438991e-01 FALSE
## 76: 3.684986e-03 2.210992e-02 TRUE
## 77: 1.801136e-01 3.833964e-01 FALSE
## 78: 3.928403e-02 1.508214e-01 FALSE
## 79: 6.216668e-01 6.538690e-01 FALSE
## 80: 5.166519e-01 6.008782e-01 FALSE
## 81: 4.890864e-02 1.711803e-01 FALSE
## 82: 6.419277e-02 2.156877e-01 FALSE
## 83: 8.842031e-01 8.948561e-01 FALSE
## 84: 1.088881e-02 5.716625e-02 FALSE
## pvalue padj Significantresdt.dds.Narrow.Broad.d0## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0.0000000 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0.0000000 NA
## 3: Fusobacteriaceae Fusobacterium <NA> 0.1663214 -0.06235141
## 4: Rikenellaceae <NA> <NA> 0.0000000 NA
## 5: Eubacteriaceae Eubacterium <NA> 0.0000000 NA
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0.0000000 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0.0000000 NA
## 1587: Prevotellaceae Prevotella <NA> 0.0000000 NA
## 1588: Prevotellaceae Prevotella bivia 0.3416447 -0.83260200
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: 3.021907 -0.02063313 0.9835383 NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: NA NA NA NA NA
## 1588: 3.011107 -0.27651024 0.7821562 NA NAvolcano.Narrow.Broad.d0 = ggplot(
data = resdt.dds.Narrow.Broad.d0[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.Narrow.Broad.d0[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 0 Narrow Spectrum vs Broad Spectrum") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d0
summary(res.dds.Narrow.Broad.d0)##
## out of 170 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 4, 2.4%
## LFC < 0 (down) : 15, 8.8%
## outliers [1] : 0, 0%
## low counts [2] : 205, 121%
## (mean count < 1)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.Narrow.Broad.d0, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.Narrow.Broad.d0)# Differential Abundance Testing
sample_data(ps1.NvB.7)$randomization_arm## [1] Narrow spectrum antibiotics Broad spectrum antibiotics
## [3] Broad spectrum antibiotics Narrow spectrum antibiotics
## [5] Broad spectrum antibiotics Narrow spectrum antibiotics
## [7] Narrow spectrum antibiotics Broad spectrum antibiotics
## [9] Narrow spectrum antibiotics Broad spectrum antibiotics
## [11] Narrow spectrum antibiotics Broad spectrum antibiotics
## [13] Narrow spectrum antibiotics Narrow spectrum antibiotics
## [15] Narrow spectrum antibiotics Broad spectrum antibiotics
## [17] Narrow spectrum antibiotics Broad spectrum antibiotics
## [19] Broad spectrum antibiotics Narrow spectrum antibiotics
## [21] Broad spectrum antibiotics Narrow spectrum antibiotics
## [23] Narrow spectrum antibiotics Broad spectrum antibiotics
## [25] Broad spectrum antibiotics Narrow spectrum antibiotics
## [27] Narrow spectrum antibiotics Broad spectrum antibiotics
## Levels: Narrow spectrum antibiotics Broad spectrum antibioticssample_data(ps1.NvB.7)$day## [1] 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7ds.Narrow.Broad.d7 <- phyloseq_to_deseq2(ps1.NvB.7, ~randomization_arm)
geoMeans.ds.Narrow.Broad.d7 <- apply(counts(ds.Narrow.Broad.d7), 1, gm_mean)
ds.Narrow.Broad.d7 <- estimateSizeFactors(ds.Narrow.Broad.d7, geoMeans = geoMeans.ds.Narrow.Broad.d7)
dds.Narrow.Broad.d7 <- DESeq(ds.Narrow.Broad.d7, test="Wald", fitType="local", betaPrior = FALSE)
# Tabulate and write results
res.dds.Narrow.Broad.d7 = results(dds.Narrow.Broad.d7, cooksCutoff = FALSE)
sigtab_dds.dds.Narrow.Broad.d7 = res.dds.Narrow.Broad.d7[which(res.dds.Narrow.Broad.d7$padj < alpha), ]
sigtab_dds.dds.Narrow.Broad.d7 = cbind(as(sigtab_dds.dds.Narrow.Broad.d7, "data.frame"), as(tax_table(ps1.NvB_9)[rownames(sigtab_dds.dds.Narrow.Broad.d7), ], "matrix"))
summary(res.dds.Narrow.Broad.d7)##
## out of 464 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 3, 0.65%
## LFC < 0 (down) : 15, 3.2%
## outliers [1] : 0, 0%
## low counts [2] : 730, 157%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultshead(sigtab_dds.dds.Narrow.Broad.d7)## baseMean
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 1561.11009
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT 74.50843
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 11.39754
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 15.13717
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 27.85206
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 35.41294
## log2FoldChange
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG -11.846096
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT -25.602786
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT -23.045137
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -23.428026
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -24.261047
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG -6.297244
## lfcSE
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 2.161968
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT 2.947715
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 2.948489
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 2.948263
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 2.893439
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 2.152931
## stat
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG -5.479312
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT -8.685638
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT -7.815913
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT -7.946383
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT -8.384848
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG -2.924964
## pvalue
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 4.269838e-08
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT 3.766219e-18
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 5.456586e-15
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 1.920367e-15
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 5.079129e-17
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 3.444963e-03
## padj
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG 4.818817e-07
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT 5.950626e-16
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT 1.231629e-13
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT 7.585451e-14
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT 4.012512e-15
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG 3.628694e-02
## Kingdom
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Bacteria
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Bacteria
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Bacteria
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteria
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Bacteria
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Bacteria
## Phylum
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobia
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Firmicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Firmicutes
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidetes
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Firmicutes
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Actinobacteria
## Class
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiae
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Negativicutes
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Clostridia
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidia
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridia
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Actinobacteria
## Order
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiales
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Selenomonadales
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Clostridiales
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidales
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Clostridiales
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Bifidobacteriales
## Family
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Verrucomicrobiaceae
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Veillonellaceae
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Ruminococcaceae
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroidaceae
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Ruminococcaceae
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Bifidobacteriaceae
## Genus
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG Akkermansia
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT Megamonas
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT Ruminococcus
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT Bacteroides
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT Faecalibacterium
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG Bifidobacterium
## Species
## TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG muciniphila
## GCAAGCGTTGTCCGGAATTATTGGGCGTAAAGGGAGCGCAGGCGGGAAACTAAGCGGATCTTAAAAGTGCGGGGCTCAACCCCGTGATGGGGTCCGAACTGGTTTTCTTGAGTGCAGGAGAGGAAAGCGGAATTCCCAGTGTAGCGGTGAAATGCGTAGATATTGGGAAGAACACCAGTGGCGAAGGCGGCTTTCTGGACTGTAACTGACGCTGAGGCTCGAAAGCTAGGGT funiformis
## GCAAGCGTTGTCCGGATTTACTGGGTGTAAAGGGTGCGTAGGCGGCTTTGCAAGTCAGATGTGAAATCTATGGGCTCAACCCATAGCCTGCATTTGAAACTGCAGAGCTTGAGTGAAGTAGAGGCAGGCGGAATTCCCCGTGTAGCGGTGAAATGCGTAGAGATGGGGAGGAACACCAGTGGCGAAGGCGGCCTGCTGGGCTTTAACTGACGCTGAGGCACGAAAGCGTGGGT <NA>
## CCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGATGGATGTTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGATGTCTTGAGTGCAGTTGAGGCAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCCTGCTAAGCTGCAACTGACATTGAGGCTCGAAAGTGTGGGT dorei
## ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT prausnitzii
## GCAAGCGTTATCCGGAATTATTGGGCGTAAAGGGCTCGTAGGCGGTTCGTCGCGTCCGGTGTGAAAGTCCATCGCTTAACGGTGGATCCGCGCCGGGTACGGGCGGGCTTGAGTGCGGTAGGGGAGACTGGAATTCCCGGTGTAACGGTGGAATGTGTAGATATCGGGAAGAACACCAATGGCGAAGGCAGGTCTCTGGGCCGTCACTGACGCTGAGGAGCGAAAGCGTGGGG <NA>write.table(sigtab_dds.dds.Narrow.Broad.d7, file="./results/deseq_d7_Narrow.Broad.txt", sep = "\t") # Change filename to save results to appropriate file
# Quick check of factor levels
mcols(res.dds.Narrow.Broad.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-values# Prepare to join results data.table and taxonomy table
resdt.dds.Narrow.Broad.d7 = data.table(as(results(dds.Narrow.Broad.d7, cooksCutoff = FALSE), "data.frame"),
keep.rownames = TRUE)
setnames(resdt.dds.Narrow.Broad.d7, "rn", "OTU")
taxdt.dds.d7.com = data.table(data.frame(as(tax_table(ps1.NvB.7), "matrix")), keep.rownames = TRUE)
setnames(taxdt.dds.d7.com, "rn", "OTU")
# Join results data.table and taxonomy table
setkeyv(taxdt.dds.d7.com, "OTU")
setkeyv(resdt.dds.Narrow.Broad.d7, "OTU")
resdt.dds.Narrow.Broad.d7 <- taxdt.dds.d7.com[resdt.dds.Narrow.Broad.d7]
resdt.dds.Narrow.Broad.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 0
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 0
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0 NA
## 1587: Prevotellaceae Prevotella <NA> 0 0
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj
## 1: NA NA NA NA
## 2: 0 0 1 NA
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: 0 0 1 NA
## ---
## 1584: NA NA NA NA
## 1585: NA NA NA NA
## 1586: NA NA NA NA
## 1587: 0 0 1 NA
## 1588: NA NA NA NAresdt.dds.Narrow.Broad.d7[, Significant := padj < alpha]
resdt.dds.Narrow.Broad.d7[!is.na(Significant)]## OTU
## 1: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAAGACAAGTTGGAAGTGAAATCTATGGGCTCAACCCATAAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 3: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGAGAACAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGTTTTTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 4: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGATTGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 5: ACAAGCGTTGTCCGGAATTACTGGGTGTAAAGGGAGCGCAGGCGGGCGATCAAGTTGGAAGTGAAATCCATGGGCTCAACCCATGAACTGCTTTCAAAACTGGTCGTCTTGAGTAGTGCAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## ---
## 154: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTCAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGGAGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTTGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 155: GCTAGCGTTATCCGGATTTACTGGGCGTAAAGGGTGCGTAGGCGGTCTTTTAAGTCAGGAGTGAAAGGCTACGGCTCAACCGTAGTAAGCTCTTGAAACTGGAGGACTTGAGTGCAGGAGAGGAGAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTAGCGAAGGCGGCTCTCTGGACTGTAACTGACGCTGAGGCACGAAAGCGTGGGG
## 156: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATCCCGGGGCTTAACTCCGGAACTGCCTCTAATACTGTTAGACTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 157: TCAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCGGTTTGATAAGTTAGAGGTGAAATTTCGGGGCTCAACCCTGAACGTGCCTCTAATACTGTTGAGCTAGAGAGTAGTTGCGGTAGGCGGAATGTATGGTGTAGCGGTGAAATGCTTAGAGATCATACAGAACACCGATTGCGAAGGCAGCTTACCAAACTATATCTGACGTTGAGGCACGAAAGCGTGGGG
## 158: TCAAGCGTTGTTCGGAATCACTGGGCGTAAAGCGTGCGTAGGCTGTTTCGTAAGTCGTGTGTGAAAGGCGCGGGCTCAACCCGCGGACGGCACATGATACTGCGAGACTAGAGTAATGGAGGGGGAACCGGAATTCTCGGTGTAGCAGTGAAATGCGTAGATATCGAGAGGAACACTCGTGGCGAAGGCGGGTTCCTGGACATTAACTGACGCTGAGGCACGAAGGCCAGGGG
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Firmicutes Clostridia Clostridiales
## 3: Bacteria Firmicutes Clostridia Clostridiales
## 4: Bacteria Firmicutes Clostridia Clostridiales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 154: Bacteria Firmicutes Clostridia Clostridiales
## 155: Bacteria Firmicutes Clostridia Clostridiales
## 156: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 157: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 158: Bacteria Verrucomicrobia Verrucomicrobiae Verrucomicrobiales
## Family Genus Species baseMean
## 1: Ruminococcaceae Faecalibacterium prausnitzii 301.237846
## 2: Ruminococcaceae Faecalibacterium prausnitzii 200.774916
## 3: Ruminococcaceae Faecalibacterium prausnitzii 16.555778
## 4: Ruminococcaceae Faecalibacterium prausnitzii 24.045248
## 5: Ruminococcaceae Faecalibacterium prausnitzii 27.852059
## ---
## 154: Peptostreptococcaceae Clostridium_XI difficile 4.685327
## 155: Peptostreptococcaceae Intestinibacter bartlettii 59.763781
## 156: Rikenellaceae Alistipes <NA> 3.771817
## 157: Rikenellaceae Alistipes putredinis 4.891952
## 158: Verrucomicrobiaceae Akkermansia muciniphila 1561.110088
## log2FoldChange lfcSE stat pvalue padj
## 1: -3.9563228 2.503057 -1.5805965 1.139703e-01 3.865438e-01
## 2: -1.6000654 1.888829 -0.8471201 3.969282e-01 7.938563e-01
## 3: -2.3993039 2.318400 -1.0348965 3.007172e-01 6.599073e-01
## 4: -1.1036476 2.897566 -0.3808878 7.032865e-01 9.183410e-01
## 5: -24.2610472 2.893439 -8.3848481 5.079129e-17 4.012512e-15
## ---
## 154: 0.5088610 2.908695 0.1749448 8.611230e-01 9.721283e-01
## 155: -0.6545537 1.109745 -0.5898234 5.553091e-01 8.127672e-01
## 156: -2.0639050 2.921048 -0.7065633 4.798379e-01 8.127672e-01
## 157: -5.2204808 2.949606 -1.7698907 7.674535e-02 3.071502e-01
## 158: -11.8460961 2.161968 -5.4793116 4.269838e-08 4.818817e-07
## Significant
## 1: FALSE
## 2: FALSE
## 3: FALSE
## 4: FALSE
## 5: TRUE
## ---
## 154: FALSE
## 155: FALSE
## 156: FALSE
## 157: FALSE
## 158: TRUEresdt.dds.Narrow.Broad.d7## OTU
## 1: ACAAGCGTTATCCGGATTTACTGGGTGTAAAGGGCGCGCAGGCGGGCCGGCAAGTTGGAAGTGAAATCTATGGGCTTAACCCATAAACTGCTTTCAAAACTGCTGGTCTTGAGTGATGGAGAGGTAGGCGGAATTCCCGGTGTAGCGGTGGAATGCGTAGATATCGGGAGGAACACCAGTGGCGAAGGCGGCCTACTGGGCACCAACTGACGCTGAGGCTCGAAAGTGTGGGT
## 2: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATATAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGTGTAACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTGAAGCGCGAAAGCGTGGGT
## 3: ACAAGCGTTATCCGGATTTATTGGGCGTAAAGCGCGTCTAGGTGGTTATGTAAGTCTGATGTGAAAATGCAGGGCTCAACTCTGTATTGCGTTGGAAACTGCATGACTAGAGTACTGGAGAGGTAAGCGGAACTACAAGTGTAGAGGTGAAATTCGTAGATATTTGTAGGAATGCCGATGGGGAAGCCAGCTTACTGGACAGATACTGACGCTAAAGCGCGAAAGCGTGGGT
## 4: ACAAGCGTTATCCGGATTTATTGGGTTTAAAGGGTGCGTAGGCTGTTTTTTAAGTTAGAGGTGAAAGCTCGACGCTCAACGTCGAAATTGCCTCTGATACTGAGAGACTAGAGTGTAGTTGCGGAAGGCGGAATGTGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 5: ACAAGCGTTGTCCGGAATGACTGGGCGTAAAGGGCGCGTAGGCGGTCTATTAAGTCTGATGTGAAAGGTACCGGCTCAACCGGTGAAGTGCATTGGAAACTGGTAGACTTGAGTATTGGAGAGGCAAGTGGAATTCCTAGTGTAGCGGTGAAATGCGTAGATATTAGGAGGAACACCAGTGGCGAAGGCGGCTTGCTGGACAAATACTGACGCTGAGGTGCGAAAGCGTGGGG
## ---
## 1584: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1585: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGAAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1586: TCGAGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCGGGTTGTTAAGTCAGTTGTGGAAGTTTGCGGCTCAACCGTAAAATTGCAGTTGATACTGGCGACCTTGAGTGCAACAGAGGTAGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTTACTGGATTGTAACTGACGCTGATGCTCGAAAGTGTGGGT
## 1587: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGGAGATTAAGCGTGTTGTGAAATGTAGATGCTCAACATCTGCACTGCAGCGCGAACTGGTTTCCTTGAGTACGCACAAAGTGGGCGGAATTCGTGGTGTAGCGGTGAAATGCTTAGATATCACGAAGAACTCCGATTGCGAAGGCAGCTCACTGGAGCGCAACTGACGCTGAAGCTCGAAAGTGCGGGT
## 1588: TCGGGCGTTATCCGGATTTATTGGGTTTAAAGGGAGCGTAGGCCGTTTGGTAAGCGTGTTGTGAAATGTAGGAGCTCAACTTCTAGATTGCAGCGCGAACTGTCAGACTTGAGTGCGCACAACGTAGGCGGAATTCATGGTGTAGCGGTGAAATGCTTAGATATCATGAAGAACTCCGATTGCGAAGGCAGCTTACGGGAGCGCAACTGACGCTGAAGCTCGAAGGTGCGGGT
## Kingdom Phylum Class Order
## 1: Bacteria Firmicutes Clostridia Clostridiales
## 2: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 3: Bacteria Fusobacteria Fusobacteriia Fusobacteriales
## 4: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 5: Bacteria Firmicutes Clostridia Clostridiales
## ---
## 1584: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1585: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1586: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1587: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## 1588: Bacteria Bacteroidetes Bacteroidia Bacteroidales
## Family Genus Species baseMean log2FoldChange
## 1: Ruminococcaceae <NA> <NA> 0 NA
## 2: Fusobacteriaceae Fusobacterium nucleatum 0 0
## 3: Fusobacteriaceae Fusobacterium <NA> 0 NA
## 4: Rikenellaceae <NA> <NA> 0 NA
## 5: Eubacteriaceae Eubacterium <NA> 0 0
## ---
## 1584: Bacteroidaceae Bacteroides <NA> 0 NA
## 1585: Bacteroidaceae Bacteroides stercoris 0 NA
## 1586: Bacteroidaceae Bacteroides <NA> 0 NA
## 1587: Prevotellaceae Prevotella <NA> 0 0
## 1588: Prevotellaceae Prevotella bivia 0 NA
## lfcSE stat pvalue padj Significant
## 1: NA NA NA NA NA
## 2: 0 0 1 NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: 0 0 1 NA NA
## ---
## 1584: NA NA NA NA NA
## 1585: NA NA NA NA NA
## 1586: NA NA NA NA NA
## 1587: 0 0 1 NA NA
## 1588: NA NA NA NA NAvolcano.Narrow.Broad.d7 = ggplot(
data = resdt.dds.Narrow.Broad.d7[!is.na(Significant)][(pvalue < 1)],
mapping = aes(x = log2FoldChange,
y = -log10(pvalue),
color = Phylum,
label = OTU, label1 = Genus)) +
theme_bw() +
geom_point() +
geom_point(data = resdt.dds.Narrow.Broad.d7[(Significant)], size = 7, alpha = 0.7) +
# geom_text(data = resdt[(Significant)], mapping = aes(label = paste("Genus:", Genus)), color = "black", size = 3) +
theme(axis.text.x = element_text(angle = -90, hjust = 0, vjust=0.5)) +
geom_hline(yintercept = -log10(alpha)) +
ggtitle("DESeq2 Negative Binomial Test Volcano Plot\nDay 7 Narrow Spectrum vs Broad Spectrum") +
theme(axis.title = element_text(size=12)) +
theme(axis.text = element_text(size=12)) +
theme(legend.text = element_text(size=12)) +
geom_vline(xintercept = 0, lty = 2)
volcano.Narrow.Broad.d7
summary(res.dds.Narrow.Broad.d7)##
## out of 464 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 3, 0.65%
## LFC < 0 (down) : 15, 3.2%
## outliers [1] : 0, 0%
## low counts [2] : 730, 157%
## (mean count < 2)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?resultsmcols(res.dds.Narrow.Broad.d7, use.names = TRUE)## DataFrame with 6 rows and 2 columns
## type
## <character>
## baseMean intermediate
## log2FoldChange results
## lfcSE results
## stat results
## pvalue results
## padj results
## description
## <character>
## baseMean mean of normalized counts for all samples
## log2FoldChange log2 fold change (MLE): randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## lfcSE standard error: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## stat Wald statistic: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## pvalue Wald test p-value: randomization arm Broad.spectrum.antibiotics vs Narrow.spectrum.antibiotics
## padj BH adjusted p-valuesggplotly(volcano.Narrow.Broad.d7)# Control vs. Narrow D_9
nrow(res.dds.NoAbx.Narrow.d_9)## [1] 1588df1 <- as.data.frame(res.dds.NoAbx.Narrow.d_9[ which(res.dds.NoAbx.Narrow.d_9$padj < 0.05), ])
nrow(df1)## [1] 6df1 <- as.data.frame(df1[ which(df1$baseMean > 100), ])
nrow(df1)## [1] 0df1 <- rownames_to_column(df1, var = "ASV")
# df1$Comparison <- "Control_v_Narrow_d_9" errors out as there are no taxa
write.table(df1, file = "./Results/df1.txt", sep = "\t")
# Control vs. Narrow D0
nrow(res.dds.NoAbx.Narrow.d0)## [1] 1588df2 <- as.data.frame(res.dds.NoAbx.Narrow.d0[ which(res.dds.NoAbx.Narrow.d0$padj < 0.05), ])
nrow(df2)## [1] 115df2 <- as.data.frame(df2[ which(df2$baseMean > 100), ])
nrow(df2)## [1] 21df2 <- rownames_to_column(df2, var = "ASV")
df2$Comparison <- "Control_v_Narrow_d0"
write.table(df2, file = "./Results/df2.txt", sep = "\t")
# Control vs. Narrow D7
nrow(res.dds.NoAbx.Narrow.d7)## [1] 1588df3 <- as.data.frame(res.dds.NoAbx.Narrow.d7[ which(res.dds.NoAbx.Narrow.d7$padj < 0.05), ])
nrow(df3)## [1] 11df3 <- as.data.frame(df3[ which(df3$baseMean > 100), ])
nrow(df3)## [1] 0df3 <- rownames_to_column(df3, var = "ASV")
# df3$Comparison <- "Control_v_Narrow_d7" errors out as there are no taxa
write.table(df3, file = "./Results/df3.txt", sep = "\t")
# Control vs. Broad D_9
nrow(res.dds.NoAbx.Broad.d_9)## [1] 1588df4 <- as.data.frame(res.dds.NoAbx.Broad.d_9[ which(res.dds.NoAbx.Broad.d_9$padj < 0.05), ])
nrow(df4)## [1] 9df4 <- as.data.frame(df4[ which(df4$baseMean > 100), ])
nrow(df4)## [1] 1df4 <- rownames_to_column(df4, var = "ASV")
df4$Comparison <- "Control_v_Broad_d_9"
write.table(df4, file = "./Results/df4.txt", sep = "\t")
# Control vs. Broad D0
nrow(res.dds.NoAbx.Broad.d0)## [1] 1588df5 <- as.data.frame(res.dds.NoAbx.Broad.d0[ which(res.dds.NoAbx.Broad.d0$padj < 0.05), ])
nrow(df5)## [1] 67df5 <- as.data.frame(df5[ which(df5$baseMean > 100), ])
nrow(df5)## [1] 11df5 <- rownames_to_column(df5, var = "ASV")
df5$Comparison <- "Control_v_Broad_d0"
write.table(df5, file = "./Results/df5.txt", sep = "\t")
# Control vs. Broad D7
nrow(res.dds.NoAbx.Broad.d7)## [1] 1588df6 <- as.data.frame(res.dds.NoAbx.Broad.d7[ which(res.dds.NoAbx.Broad.d7$padj < 0.05), ])
nrow(df6)## [1] 29df6 <- as.data.frame(df6[ which(df6$baseMean > 100), ])
nrow(df6)## [1] 1df6 <- rownames_to_column(df6, var = "ASV")
df6$Comparison <- "Control_v_Broad_d7"
write.table(df6, file = "./Results/df6.txt", sep = "\t")
# Narrow vs. Broad D_9
nrow(res.dds.Narrow.Broad.d_9)## [1] 1588df7 <- as.data.frame(res.dds.Narrow.Broad.d_9[ which(res.dds.Narrow.Broad.d_9$padj < 0.05), ])
nrow(df7)## [1] 11df7 <- as.data.frame(df7[ which(df7$baseMean > 100), ])
nrow(df7)## [1] 0df7 <- rownames_to_column(df7, var = "ASV")
#df7$Comparison <- "Narrow_v_Broad_d_9" errors out as there are no taxa
write.table(df7, file = "./Results/df7.txt", sep = "\t")
# Narrow vs. Broad D0
nrow(res.dds.Narrow.Broad.d0)## [1] 1588df8 <- as.data.frame(res.dds.Narrow.Broad.d0[ which(res.dds.Narrow.Broad.d0$padj < 0.05), ])
nrow(df8)## [1] 14df8 <- as.data.frame(df8[ which(df8$baseMean > 100), ])
nrow(df8)## [1] 6df8 <- rownames_to_column(df8, var = "ASV")
df8$Comparison <- "Narrow_v_Broad_d0"
write.table(df8, file = "./Results/df8.txt", sep = "\t")
# Narrow vs. Broad D7
nrow(res.dds.Narrow.Broad.d7)## [1] 1588df9 <- as.data.frame(res.dds.Narrow.Broad.d7[ which(res.dds.Narrow.Broad.d7$padj < 0.05), ])
nrow(df9)## [1] 15df9 <- as.data.frame(df9[ which(df9$baseMean > 100), ])
nrow(df9)## [1] 1df9 <- rownames_to_column(df9, var = "ASV")
df9$Comparison <- "Narrow_v_Broad_d7"
write.table(df9, file = "./Results/df9.txt", sep = "\t")
# Combine all differential abundance tables
df.all <- rbind(df1, df2, df3, df4, df5, df6, df7, df8, df9)
nrow(df.all)## [1] 41write.table(df.all, file = "./Results/df_all.txt", sep = "\t")
# Create table of unique differentially abundant ASV
dfs <- list(df1, df2, df3, df4, df5, df6, df7, df8, df9)
df.unique <- join_all(dfs, type = "full", by = "ASV")
nrow(df.unique)## [1] 28write.table(df.unique, file = "./Results/df_unique.txt", sep = "\t")Bind taxonomy to results
# Load other taxonomies preprocessed with dada2
ps1.rdp <- readRDS("./data/ps0.human_volunteer.rdp.RDS")
# Create appropirately formated taxa table
# RDP
ps1.rdp.tax <- as.tibble(as.data.frame(tax_table(ps1.rdp)))
ps1.rdp.tax <- rownames_to_column(ps1.rdp.tax, var = "ASV")
df.all.rdp <- left_join(df.all, ps1.rdp.tax, by = "ASV")
write.table(df.all.rdp, file = "./results/df_all_rdp.txt", sep = "\t")
df.unique.rdp <- left_join(df.unique, ps1.rdp.tax, by = "ASV")
write.table(df.unique.rdp, file = "./results/df_unique_rdp.txt", sep = "\t")Individual ASV plots
##Ground truth plots
replace_counts = function(physeq, dds) {
dds_counts = counts(dds, normalized = TRUE)
if (!identical(taxa_names(physeq), rownames(dds_counts))) {
stop("OTU ids don't match")
}
otu_table(physeq) = otu_table(dds_counts, taxa_are_rows = TRUE)
return(physeq)
}
# Make deseq ready object
ds.all <- phyloseq_to_deseq2(ps1, ~randomization_arm)
geoMeans.all <- apply(counts(ds.all), 1, gm_mean)
ds.all <- estimateSizeFactors(ds.all, geoMeans = geoMeans.all)
dds.all <- DESeq(ds.all, test="Wald", betaPrior = TRUE)
rlog.all <- replace_counts(ps1, dds.all)
rlog.all <- psmelt(rlog.all)
# Need to change Day to numeric
class(rlog.all$day)## [1] "integer"rlog.all$Day <- as.numeric(as.character(rlog.all$day))
class(rlog.all$day)## [1] "integer"# Rename OTU to ASV
rlog.all <- rename(rlog.all, c("OTU" = "ASV"))
# Select out taxa of interest from rlog.all
rlog.all.rdp <- inner_join(df.all.rdp, rlog.all, by = "ASV")
rlog.unique.rdp <- inner_join(df.unique.rdp, rlog.all, by = "ASV")
# Note: ggplot2 will not plot smoothers for individual groups if the first smoother "fails"
# to adjust for this stat_smooth is applied to each individual groups
p.unique.rdp <- ggplot(rlog.all.rdp, aes(x = day, y = Abundance, group = randomization_arm, color = randomization_arm)) +
geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
scale_y_log10() +
theme(legend.position = "NULL") +
facet_wrap(Phylum.x~Family.x) +
stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "Narrow spectrum antibiotics", method = "loess")) +
stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "Broad spectrum antibiotics", method = "loess")) +
stat_smooth(data = subset(rlog.all.rdp, randomization_arm == "No antibiotics", method = "loess")) +
labs(color = "Treatment", x = "Day", y = "Abundance (rlog)")
p.unique.rdp
# Combine summary plots with > 0 taxa
dfs.2 <- list(df2, df4, df5, df6, df8, df9)
df.all.2 <- join_all(dfs.2, type = "full", by = "Comparison")
nrow(df.all.2)## [1] 41# Bind taxonomu
df.all.rdp.2 <- left_join(df.all.2, ps1.rdp.tax, by = "ASV")
# Select from rlog normalized data
dfs.all.rdp.2 <- inner_join(df.all.2, rlog.all, by = "ASV")
# Adjust factor levels to order by time point (roughly)
df.all.rdp.2$Comparison <- as.factor(df.all.rdp.2$Comparison)
levels(df.all.rdp.2$Comparison)## [1] "Control_v_Broad_d_9" "Control_v_Broad_d0" "Control_v_Broad_d7"
## [4] "Control_v_Narrow_d0" "Narrow_v_Broad_d0" "Narrow_v_Broad_d7"df.all.rdp.2$Comparison <- factor(df.all.rdp.2$Comparison, levels = c("Control_v_Broad_d_9", "Control_v_Narrow_d0", "Control_v_Broad_d0", "Narrow_v_Broad_d0", "Control_v_Broad_d7", "Narrow_v_Broad_d7"))
levels(df.all.rdp.2$Comparison)## [1] "Control_v_Broad_d_9" "Control_v_Narrow_d0" "Control_v_Broad_d0"
## [4] "Narrow_v_Broad_d0" "Control_v_Broad_d7" "Narrow_v_Broad_d7"df.all.rdp.2$Comparison <- factor(df.all.rdp.2$Comparison, labels = c("Control v. Broad: Day -9", "Control v. Narrow: Day 0", "Control v. Broad: Day 0", "Broad v Narrow: Day 0", "Control v. Broad: Day 7", "Broad v. Narrow: Day 7"))
df.all.rdp.2$Comparison## [1] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [3] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [5] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [7] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [9] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [11] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [13] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [15] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [17] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [19] Control v. Narrow: Day 0 Control v. Narrow: Day 0
## [21] Control v. Narrow: Day 0 Control v. Broad: Day -9
## [23] Control v. Broad: Day 0 Control v. Broad: Day 0
## [25] Control v. Broad: Day 0 Control v. Broad: Day 0
## [27] Control v. Broad: Day 0 Control v. Broad: Day 0
## [29] Control v. Broad: Day 0 Control v. Broad: Day 0
## [31] Control v. Broad: Day 0 Control v. Broad: Day 0
## [33] Control v. Broad: Day 0 Control v. Broad: Day 7
## [35] Broad v Narrow: Day 0 Broad v Narrow: Day 0
## [37] Broad v Narrow: Day 0 Broad v Narrow: Day 0
## [39] Broad v Narrow: Day 0 Broad v Narrow: Day 0
## [41] Broad v. Narrow: Day 7
## 6 Levels: Control v. Broad: Day -9 ... Broad v. Narrow: Day 7p.volcano.summary.rdp <- ggplot(df.all.rdp.2, aes(x = log2FoldChange, y = -log10(padj), color = Comparison, size = log10(baseMean))) +
geom_point(alpha = 0.7) +
geom_vline(xintercept = 0, linetype = "dashed", color = "dark grey", lwd = 1) +
facet_wrap(Phylum~Family, ncol = 3) +
annotate("text", x = -15, y = 27, label = "Control") +
annotate("text", x = 15, y = 27, label = "Treated") +
labs(color = "Comparison") +
labs(x=expression(log[2]*" fold-change")) +
labs(y=expression(-log[10]*" adjusted p-value")) +
labs(size=expression(log[10]*" base mean")) +
scale_color_brewer(palette = "Dark2") +
scale_y_continuous(limits = c(0,30), expand = c(0, 0)) +
guides(colour = guide_legend(override.aes = list(size=3))) +
theme(legend.position = "null")
p.volcano.summary.rdp
# Interctive plot
ggplotly(p.volcano.summary.rdp)ggarrange(p.volcano.summary.rdp, p.unique.rdp, nrow = 2, labels = c("A)", "B)"), heights = c(1.3,1))
## Differential abundance for boosting at each time point
# Day -9
ds.boost.d_9 <- phyloseq_to_deseq2(ps1.d_9, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d_9 <- apply(counts(ds.boost.d_9), 1, gm_mean)
ds.boost.d_9 <- estimateSizeFactors(ds.boost.d_9, geoMeans = geoMeans.ds.boost.d_9)
dds.boost.d_9 <- DESeq(ds.boost.d_9, test="Wald")
res.dds.boost.d_9 <- results(dds.boost.d_9, cooksCutoff = FALSE)
summary(res.dds.boost.d_9)##
## out of 686 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 71, 10%
## LFC < 0 (down) : 31, 4.5%
## outliers [1] : 0, 0%
## low counts [2] : 145, 21%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Day 0
ds.boost.d0 <- phyloseq_to_deseq2(ps1.d0, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d0 <- apply(counts(ds.boost.d0), 1, gm_mean)
ds.boost.d0 <- estimateSizeFactors(ds.boost.d0, geoMeans = geoMeans.ds.boost.d0)
dds.boost.d0 <- DESeq(ds.boost.d0, test="Wald")
res.dds.boost.d0 = results(dds.boost.d0, cooksCutoff = FALSE)
summary(res.dds.boost.d0)##
## out of 524 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 71, 14%
## LFC < 0 (down) : 13, 2.5%
## outliers [1] : 0, 0%
## low counts [2] : 192, 37%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Day 7
ds.boost.d7 <- phyloseq_to_deseq2(ps1.d7, ~randomization_arm + d7_rota_boost_updated)
geoMeans.ds.boost.d7 <- apply(counts(ds.boost.d7), 1, gm_mean)
ds.boost.d7 <- estimateSizeFactors(ds.boost.d7, geoMeans = geoMeans.ds.boost.d7)
dds.boost.d7 <- DESeq(ds.boost.d7, test="Wald")
res.dds.boost.d7 = results(dds.boost.d7, cooksCutoff = FALSE)
summary(res.dds.boost.d7)##
## out of 792 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 136, 17%
## LFC < 0 (down) : 19, 2.4%
## outliers [1] : 0, 0%
## low counts [2] : 414, 52%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Tabulate results
# Day -9
nrow(res.dds.boost.d_9)## [1] 1588df1.boost <- as.data.frame(res.dds.boost.d_9[ which(res.dds.boost.d_9$padj < 0.05), ])
nrow(df1.boost)## [1] 99df1.boost <- as.data.frame(df1.boost[ which(df1.boost$baseMean > 100), ])
nrow(df1.boost)## [1] 9df1.boost <- rownames_to_column(df1.boost, var = "ASV")
df1.boost$Comparison <- "Boost: Day -9"
df1.boost$Variable <- "Boost"
# Day 0
nrow(res.dds.boost.d0)## [1] 1588df2.boost <- as.data.frame(res.dds.boost.d0[ which(res.dds.boost.d0$padj < 0.05), ])
nrow(df2.boost)## [1] 84df2.boost <- as.data.frame(df2.boost[ which(df2.boost$baseMean > 100), ])
nrow(df2.boost)## [1] 2df2.boost <- rownames_to_column(df2.boost, var = "ASV")
df2.boost$Comparison <- "Boost: Day 0"
df2.boost$Variable <- "Boost"
# Day 7
nrow(res.dds.boost.d7)## [1] 1588df3.boost <- as.data.frame(res.dds.boost.d7[ which(res.dds.boost.d7$padj < 0.05), ])
nrow(df3.boost)## [1] 150df3.boost <- as.data.frame(df3.boost[ which(df3.boost$baseMean > 100), ])
nrow(df3.boost)## [1] 9df3.boost <- rownames_to_column(df3.boost, var = "ASV")
df3.boost$Comparison <- "Boost: Day 7"
df3.boost$Variable <- "Boost"
# Combine all differential abundance tables
df.all.boost <- rbind(df1.boost, df2.boost, df3.boost)
nrow(df.all.boost)## [1] 20dfs.boost <- list(df1.boost, df2.boost, df3.boost)
df.unique.boost <- join_all(dfs.boost, type = "full", by = "ASV")
nrow(df.unique.boost)## [1] 17# Bind taxonomy
df.all.boost <- left_join(df.all.boost, ps1.rdp.tax, by = "ASV")
df.unique.boost <- left_join(df.unique.boost, ps1.rdp.tax, by = "ASV")
write.table(df.unique.boost, file = "./results/df_unique_boost.txt", sep = "\t")
write.table(df.unique.boost, file = "./results/df_all_boost.txt", sep = "\t")
p.boost.point <- ggplot(subset(df.unique.boost, Comparison != "Boost: Day -9"), aes(x = log2FoldChange, y = Family, color = Phylum)) +
geom_point(aes(size = log10(baseMean)), alpha = 0.7) +
facet_grid(~Comparison) +
geom_vline(xintercept = 0, lty = 2) +
theme(legend.position = "NULL") +
xlim(-40, 40) +
scale_color_brewer(palette = "Dark2") +
geom_errorbarh(aes(xmax = log2FoldChange + lfcSE, xmin = log2FoldChange - lfcSE), size=0.5, height = 0.3) +
labs(x=expression(log[2]*" fold-change")) +
labs(size=expression(log[10]*" base mean"))
p.boost.point
## Differential abundance for shedding at each time point
# Day -9
ds.shed.d_9 <- phyloseq_to_deseq2(ps1.d_9, ~randomization_arm + Shedding)
geoMeans.ds.shed.d_9 <- apply(counts(ds.shed.d_9), 1, gm_mean)
ds.shed.d_9 <- estimateSizeFactors(ds.shed.d_9, geoMeans = geoMeans.ds.shed.d_9)
dds.shed.d_9 <- DESeq(ds.shed.d_9, test="Wald")
res.dds.shed.d_9 <- results(dds.shed.d_9, cooksCutoff = FALSE)
summary(res.dds.shed.d_9)##
## out of 767 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 37, 4.8%
## LFC < 0 (down) : 97, 13%
## outliers [1] : 0, 0%
## low counts [2] : 218, 28%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Day 0
ds.shed.d0 <- phyloseq_to_deseq2(ps1.d0, ~randomization_arm + Shedding)
geoMeans.ds.shed.d0 <- apply(counts(ds.shed.d0), 1, gm_mean)
ds.shed.d0 <- estimateSizeFactors(ds.shed.d0, geoMeans = geoMeans.ds.shed.d0)
dds.shed.d0 <- DESeq(ds.shed.d0, test="Wald")
res.dds.shed.d0 = results(dds.shed.d0, cooksCutoff = FALSE)
summary(res.dds.shed.d0)##
## out of 515 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 39, 7.6%
## LFC < 0 (down) : 22, 4.3%
## outliers [1] : 0, 0%
## low counts [2] : 201, 39%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Day 7
ds.shed.d7 <- phyloseq_to_deseq2(ps1.d7, ~randomization_arm + Shedding)
geoMeans.ds.shed.d7 <- apply(counts(ds.shed.d7), 1, gm_mean)
ds.shed.d7 <- estimateSizeFactors(ds.shed.d7, geoMeans = geoMeans.ds.shed.d7)
dds.shed.d7 <- DESeq(ds.shed.d7, test="Wald")
res.dds.shed.d7 = results(dds.shed.d7, cooksCutoff = FALSE)
summary(res.dds.shed.d7)##
## out of 813 with nonzero total read count
## adjusted p-value < 0.1
## LFC > 0 (up) : 93, 11%
## LFC < 0 (down) : 4, 0.49%
## outliers [1] : 0, 0%
## low counts [2] : 322, 40%
## (mean count < 0)
## [1] see 'cooksCutoff' argument of ?results
## [2] see 'independentFiltering' argument of ?results# Tabulate results
# Day -9
nrow(res.dds.shed.d_9)## [1] 1588df1.shed <- as.data.frame(res.dds.shed.d_9[ which(res.dds.shed.d_9$padj < 0.05), ])
nrow(df1.shed)## [1] 130df1.shed <- as.data.frame(df1.shed[ which(df1.shed$baseMean > 100), ])
nrow(df1.shed)## [1] 8df1.shed <- rownames_to_column(df1.shed, var = "ASV")
df1.shed$Comparison <- "Shedding: Day -9"
df1.shed$Variable <- "Shedding"
# Day 0
nrow(res.dds.shed.d0)## [1] 1588df2.shed <- as.data.frame(res.dds.shed.d0[ which(res.dds.shed.d0$padj < 0.05), ])
nrow(df2.shed)## [1] 61df2.shed <- as.data.frame(df2.shed[ which(df2.shed$baseMean > 100), ])
nrow(df2.shed)## [1] 4df2.shed <- rownames_to_column(df2.shed, var = "ASV")
df2.shed$Comparison <- "Shedding: Day 0"
df2.shed$Variable <- "Shedding"
# Day 7
nrow(res.dds.shed.d7)## [1] 1588df3.shed <- as.data.frame(res.dds.shed.d7[ which(res.dds.shed.d7$padj < 0.05), ])
nrow(df3.shed)## [1] 93df3.shed <- as.data.frame(df3.shed[ which(df3.shed$baseMean > 100), ])
nrow(df3.shed)## [1] 5df3.shed <- rownames_to_column(df3.shed, var = "ASV")
df3.shed$Comparison <- "Shedding: Day 7"
df3.shed$Variable <- "Shedding"
# Combine all differential abundance tables
df.all.shed <- rbind(df1.shed, df2.shed, df3.shed)
nrow(df.all.shed)## [1] 17dfs.shed <- list(df1.shed, df2.shed, df3.shed)
df.unique.shed <- join_all(dfs.shed, type = "full", by = "ASV")
nrow(df.unique.shed)## [1] 16# Bind taxonomy
df.all.shed <- left_join(df.all.shed, ps1.rdp.tax, by = "ASV")
df.unique.shed <- left_join(df.unique.shed, ps1.rdp.tax, by = "ASV")
write.table(df.unique.shed, file = "./results/df_unique_shed.txt", sep = "\t")
write.table(df.unique.shed, file = "./results/df_all_shed.txt", sep = "\t")
p.shed.point <- ggplot(subset(df.unique.shed, Comparison != "Shedding: Day -9"), aes(x = log2FoldChange, y = Family, color = Phylum)) +
geom_point(aes(size = log10(baseMean)), alpha = 0.7) +
facet_grid(~Comparison) +
geom_vline(xintercept = 0, lty = 2) +
xlim(-40, 40) +
theme(legend.position = "NULL") +
scale_color_brewer(palette = "Dark2") +
geom_errorbarh(aes(xmax = log2FoldChange + lfcSE, xmin = log2FoldChange - lfcSE), size=0.5, height = 0.3) +
labs(x=expression(log[2]*" fold-change")) +
labs(size=expression(log[10]*" base mean"))
p.shed.point
nrow(df.all.boost)## [1] 20nrow(df.all.shed)## [1] 17df.all.boost_shed <- rbind(df.all.boost, df.all.shed)
df.all.boost_shed.distinct <- (distinct(df.all.boost_shed, ASV, .keep_all = TRUE))
nrow(df.all.boost_shed.distinct)## [1] 23df.all.boost.counts <- inner_join(df.all.boost, rlog.all, by = "ASV")
nrow(df.all.boost.counts)## [1] 2000df.all.shed.counts <- inner_join(df.all.shed, rlog.all, by = "ASV")
nrow(df.all.shed.counts)## [1] 1700# Smoother plots
p.diff.ab.boost.smooth <- ggplot(df.all.boost.counts, aes(x = day, y = Abundance, group = d7_rota_boost_updated, color = d7_rota_boost_updated)) +
geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
scale_y_log10() +
labs(color = "Boost") +
theme(legend.position = "NULL") +
facet_wrap(Phylum.x~Family.x) +
stat_smooth(data = subset(df.all.boost.counts, d7_rota_boost_updated == "No", method = "loess")) +
stat_smooth(data = subset(df.all.boost.counts, d7_rota_boost_updated == "Yes", method = "loess")) +
labs(y = "Abundance (rlog)", x = "Day")
p.diff.ab.shed.smooth <- ggplot(df.all.shed.counts, aes(x = day, y = Abundance, group = Shedding, color = Shedding)) +
geom_jitter(size = 1, alpha = 0.6, width = 0.5) +
scale_y_log10() +
theme(legend.position = "NULL") +
facet_wrap(Phylum.x~Family.x) +
stat_smooth(data = subset(df.all.shed.counts, Shedding == "No shedding", method = "loess")) +
stat_smooth(data = subset(df.all.shed.counts, Shedding == "Shedding", method = "loess")) +
labs(y = "Abundance (rlog)", x = "Day")ggarrange(ggarrange(p.boost.point, p.shed.point, nrow = 2, labels = c("A)", "C)")),
ggarrange(p.diff.ab.boost.smooth, p.diff.ab.shed.smooth, nrow = 2, labels = c("B)", "D)")), widths = c(1,1.25), ncol = 2)
# Display current R session information
sessionInfo()## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.5
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bindrcpp_0.2.2 DESeq2_1.21.1
## [3] SummarizedExperiment_1.11.1 DelayedArray_0.7.0
## [5] BiocParallel_1.15.0 matrixStats_0.53.1
## [7] Biobase_2.41.0 GenomicRanges_1.33.1
## [9] GenomeInfoDb_1.17.0 IRanges_2.15.4
## [11] S4Vectors_0.19.2 BiocGenerics_0.27.0
## [13] pairwiseAdonis_0.0.1 cluster_2.0.7-1
## [15] data.table_1.11.4 ggpubr_0.1.6
## [17] magrittr_1.5 microbiome_1.3.0
## [19] plotly_4.7.1 knitr_1.20
## [21] gridExtra_2.3 vegan_2.5-2
## [23] lattice_0.20-35 permute_0.9-4
## [25] RColorBrewer_1.1-2 phyloseq_1.25.0
## [27] plyr_1.8.4 reshape2_1.4.3
## [29] forcats_0.3.0 stringr_1.3.1
## [31] dplyr_0.7.5 purrr_0.2.5
## [33] readr_1.1.1 tidyr_0.8.1
## [35] tibble_1.4.2 ggplot2_2.2.1
## [37] tidyverse_1.2.1
##
## loaded via a namespace (and not attached):
## [1] readxl_1.1.0 backports_1.1.2 Hmisc_4.1-1
## [4] igraph_1.2.1 lazyeval_0.2.1 splines_3.5.0
## [7] crosstalk_1.0.0 digest_0.6.15 foreach_1.4.4
## [10] htmltools_0.3.6 checkmate_1.8.5 memoise_1.1.0
## [13] Biostrings_2.49.0 annotate_1.59.0 modelr_0.1.2
## [16] colorspace_1.3-2 blob_1.1.1 rvest_0.3.2
## [19] haven_1.1.1 crayon_1.3.4 RCurl_1.95-4.10
## [22] jsonlite_1.5 genefilter_1.63.0 bindr_0.1.1
## [25] survival_2.42-3 iterators_1.0.9 ape_5.1
## [28] glue_1.2.0 gtable_0.2.0 zlibbioc_1.27.0
## [31] XVector_0.21.0 Rhdf5lib_1.3.0 scales_0.5.0
## [34] DBI_1.0.0 Rcpp_0.12.17 viridisLite_0.3.0
## [37] xtable_1.8-2 htmlTable_1.12 foreign_0.8-70
## [40] bit_1.1-14 Formula_1.2-3 htmlwidgets_1.2
## [43] httr_1.3.1 acepack_1.4.1 pkgconfig_2.0.1
## [46] XML_3.98-1.11 nnet_7.3-12 locfit_1.5-9.1
## [49] tidyselect_0.2.4 labeling_0.3 rlang_0.2.1
## [52] later_0.7.3 AnnotationDbi_1.43.0 munsell_0.4.3
## [55] cellranger_1.1.0 tools_3.5.0 cli_1.0.0
## [58] RSQLite_2.1.1 ade4_1.7-11 broom_0.4.4
## [61] evaluate_0.10.1 biomformat_1.7.0 yaml_2.1.19
## [64] bit64_0.9-7 nlme_3.1-137 mime_0.5
## [67] xml2_1.2.0 compiler_3.5.0 rstudioapi_0.7
## [70] ggsignif_0.4.0 geneplotter_1.59.0 stringi_1.2.2
## [73] highr_0.6 Matrix_1.2-14 psych_1.8.4
## [76] multtest_2.37.0 pillar_1.2.3 cowplot_0.9.2
## [79] bitops_1.0-6 httpuv_1.4.3 R6_2.2.2
## [82] latticeExtra_0.6-28 promises_1.0.1 codetools_0.2-15
## [85] MASS_7.3-50 assertthat_0.2.0 rhdf5_2.25.0
## [88] rprojroot_1.3-2 mnormt_1.5-5 GenomeInfoDbData_1.1.0
## [91] mgcv_1.8-23 hms_0.4.2 grid_3.5.0
## [94] rpart_4.1-13 rmarkdown_1.9 shiny_1.1.0
## [97] lubridate_1.7.4 base64enc_0.1-3